Local Deep Hashing Matching of Aerial Images Based on Relative Distance and Absolute Distance Constraints

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Suting Chen

2017-12-01

Full Text Available Aerial images have features of high resolution, complex background, and usually require large amounts of calculation, however, most algorithms used in matching of aerial images adopt the shallow hand-crafted features expressed as floating-point descriptors (e.g., SIFT (Scale-invariant Feature Transform, SURF (Speeded Up Robust Features, which may suffer from poor matching speed and are not well represented in the literature. Here, we propose a novel Local Deep Hashing Matching (LDHM method for matching of aerial images with large size and with lower complexity or fast matching speed. The basic idea of the proposed algorithm is to utilize the deep network model in the local area of the aerial images, and study the local features, as well as the hash function of the images. Firstly, according to the course overlap rate of aerial images, the algorithm extracts the local areas for matching to avoid the processing of redundant information. Secondly, a triplet network structure is proposed to mine the deep features of the patches of the local image, and the learned features are imported to the hash layer, thus obtaining the representation of a binary hash code. Thirdly, the constraints of the positive samples to the absolute distance are added on the basis of the triplet loss, a new objective function is constructed to optimize the parameters of the network and enhance the discriminating capabilities of image patch features. Finally, the obtained deep hash code of each image patch is used for the similarity comparison of the image patches in the Hamming space to complete the matching of aerial images. The proposed LDHM algorithm evaluates the UltraCam-D dataset and a set of actual aerial images, simulation result demonstrates that it may significantly outperform the state-of-the-art algorithm in terms of the efficiency and performance.

ACCURACY OF MEASUREMENTS IN OBLIQUE AERIAL IMAGES FOR URBAN ENVIRONMENT

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W. Ostrowski

2016-10-01

Full Text Available Oblique aerial images have been a source of data for urban areas for several years. However, the accuracy of measurements in oblique images during this time has been limited to a single meter due to the use of direct -georeferencing technology and the underlying digital elevation model. Therefore, oblique images have been used mostly for visualization purposes. This situation changed in recent years as new methods, which allowed for a higher accuracy of exterior orientation, were developed. Current developments include the process of determining exterior orientation and the previous but still crucial process of tie point extraction. Progress in this area was shown in the ISPRS/EUROSDR Benchmark on Multi-Platform Photogrammetry and is also noticeable in the growing interest in the use of this kind of imagery. The higher level of accuracy in the orientation of oblique aerial images that has become possible in the last few years should result in a higher level of accuracy in the measurements of these types of images. The main goal of this research was to set and empirically verify the accuracy of measurements in oblique aerial images. The research focused on photogrammetric measurements composed of many images, which use a high overlap within an oblique dataset and different view angles. During the experiments, two series of images of urban areas were used. Both were captured using five DigiCam cameras in a Maltese cross configuration. The tilt angles of the oblique cameras were 45 degrees, and the position of the cameras during flight used a high grade GPS/INS navigation system. The orientation of the images was set using the Pix4D Mapper Pro software with both measurements of the in-flight camera position and the ground control points (measured with GPS RTK technology. To control the accuracy, check points were used (which were also measured with GPS RTK technology. As reference data for the whole study, an area of the city-based map was used

BUILDING FAÇADE SEPARATION IN VERTICAL AERIAL IMAGES

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P. Meixner

2012-07-01

Full Text Available Three-dimensional models of urban environments have great appeal and offer promises of interesting applications. While initially it was of interest to just have such 3D data, it increasingly becomes evident that one really would like to have interpreted urban objects. To be able to interpret buildings we have to split a visible whole building block into its different single buildings. Usually this is done using cadastral information to divide the single land parcels. The problem in this case is that sometimes the building boundaries derived from the cadastre are insufficiently accurate due to several reasons like old databases with lower accuracies or inaccuracies due to transformation between two coordinate systems. For this reason it can happen that a cadastral boundary coming from an old map is displaced by up to several meters and therefore divides two buildings incorrectly. To overcome such problems we incorporate the information from vertical aerial images. We introduce a façade separation method that is able to find individual building façades using multi view stereo. The purpose is to identify the individual façades and separate them from one another before on proceeds with the analysis of a façade's details. The source was a set of overlapping, thus "redundant" vertical aerial images taken by an UltraCam digital aerial camera. Therefore in a first step we determine the building block outlines using the building classification and use the height values from the Digital Surface Model (DSM to determine approximate "façade quadrilaterals". We also incorporate height discontinuities using the height profiles along the building outlines to enhance our façade separation. In a next step we detect repeated pattern in these "façade images" and use them to separate the façades respectively building blocks from one another. We show that this method can be successfully used to separate building façades using vertical aerial images with a

Detection of High-Density Crowds in Aerial Images Using Texture Classification

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Oliver Meynberg

2016-06-01

Full Text Available Automatic crowd detection in aerial images is certainly a useful source of information to prevent crowd disasters in large complex scenarios of mass events. A number of publications employ regression-based methods for crowd counting and crowd density estimation. However, these methods work only when a correct manual count is available to serve as a reference. Therefore, it is the objective of this paper to detect high-density crowds in aerial images, where counting– or regression–based approaches would fail. We compare two texture–classification methodologies on a dataset of aerial image patches which are grouped into ranges of different crowd density. These methodologies are: (1 a Bag–of–words (BoW model with two alternative local features encoded as Improved Fisher Vectors and (2 features based on a Gabor filter bank. Our results show that a classifier using either BoW or Gabor features can detect crowded image regions with 97% classification accuracy. In our tests of four classes of different crowd-density ranges, BoW–based features have a 5%–12% better accuracy than Gabor.

Damage Degree Evaluation of Earthquake Area Using UAV Aerial Image

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Jinhong Chen

2016-01-01

Full Text Available An Unmanned Aerial Vehicle (UAV system and its aerial image analysis method are developed to evaluate the damage degree of earthquake area. Both the single-rotor and the six-rotor UAVs are used to capture the visible light image of ground targets. Five types of typical ground targets are considered for the damage degree evaluation: the building, the road, the mountain, the riverway, and the vegetation. When implementing the image analysis, first the Image Quality Evaluation Metrics (IQEMs, that is, the image contrast, the image blur, and the image noise, are used to assess the imaging definition. Second, once the image quality is qualified, the Gray Level Cooccurrence Matrix (GLCM texture feature, the Tamura texture feature, and the Gabor wavelet texture feature are computed. Third, the Support Vector Machine (SVM classifier is employed to evaluate the damage degree. Finally, a new damage degree evaluation (DDE index is defined to assess the damage intensity of earthquake. Many experiment results have verified the correctness of proposed system and method.

Moving object detection using dynamic motion modelling from UAV aerial images.

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Saif, A F M Saifuddin; Prabuwono, Anton Satria; Mahayuddin, Zainal Rasyid

2014-01-01

Motion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object robustly. Besides current research on moving object detection from UAV aerial images mostly depends on either frame difference or segmentation approach separately. There are two main purposes for this research: firstly to develop a new motion model called DMM (dynamic motion model) and secondly to apply the proposed segmentation approach SUED (segmentation using edge based dilation) using frame difference embedded together with DMM model. The proposed DMM model provides effective search windows based on the highest pixel intensity to segment only specific area for moving object rather than searching the whole area of the frame using SUED. At each stage of the proposed scheme, experimental fusion of the DMM and SUED produces extracted moving objects faithfully. Experimental result reveals that the proposed DMM and SUED have successfully demonstrated the validity of the proposed methodology.

Combining Constraint Types From Public Data in Aerial Image Segmentation

DEFF Research Database (Denmark)

Jacobsen, Thomas Stig; Jensen, Jacob Jon; Jensen, Daniel Rune

2013-01-01

We introduce a method for image segmentation that constraints the clustering with map and point data. The method is showcased by applying the spectral clustering algorithm on aerial images for building detection with constraints built from a height map and address point data. We automatically det...

BUILDING DETECTION USING AERIAL IMAGES AND DIGITAL SURFACE MODELS

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J. Mu

2017-05-01

Full Text Available In this paper a method for building detection in aerial images based on variational inference of logistic regression is proposed. It consists of three steps. In order to characterize the appearances of buildings in aerial images, an effective bag-of-Words (BoW method is applied for feature extraction in the first step. In the second step, a classifier of logistic regression is learned using these local features. The logistic regression can be trained using different methods. In this paper we adopt a fully Bayesian treatment for learning the classifier, which has a number of obvious advantages over other learning methods. Due to the presence of hyper prior in the probabilistic model of logistic regression, approximate inference methods have to be applied for prediction. In order to speed up the inference, a variational inference method based on mean field instead of stochastic approximation such as Markov Chain Monte Carlo is applied. After the prediction, a probabilistic map is obtained. In the third step, a fully connected conditional random field model is formulated and the probabilistic map is used as the data term in the model. A mean field inference is utilized in order to obtain a binary building mask. A benchmark data set consisting of aerial images and digital surfaced model (DSM released by ISPRS for 2D semantic labeling is used for performance evaluation. The results demonstrate the effectiveness of the proposed method.

Remote Sensing Image Registration Using Multiple Image Features

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Kun Yang

2017-06-01

Full Text Available Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform distance which is endowed with the intensity information is used to measure the scale space extrema. (iii To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

Unmanned Aerial Vehicles for Photogrammetry: Analysis of Orthophoto Images over the Territory of Lithuania

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J. Suziedelyte Visockiene

2016-01-01

Full Text Available It has been recently observed that aircrafts tend to be replaced by light, simple structure unmanned aerial vehicles (UAV or mini unmanned aerial vehicles (MUAV with the purpose of updating the field of aerial photogrammetry. The built-in digital photo camera takes images of the Earth’s surface. To satisfy the photogrammetric requirements of the photographic images, it is necessary to carry out the terrestrial project planning of the flight path before the flight, to select the appropriate flying height, the time for acquiring images, the speed of the UAV, and other parameters. The paper presents the results of project calculations concerning the UAV flights and the analysis of the terrestrial images acquired during the field-testing flights. The experience carried out so far in the Lithuanian landscape is shown. The taken images have been processed by PhotoMod photogrammetric system. The paper presents the results of calculation of the project values of the UAV flights taking the images by digital camera Canon S100 and the analysis of the possibilities of the UAV orthophoto images’ mode.

THE USE OF MOBILE LASER SCANNING DATA AND UNMANNED AERIAL VEHICLE IMAGES FOR 3D MODEL RECONSTRUCTION

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L. Zhu

2013-08-01

Full Text Available The increasing availability in multiple data sources acquired by different sensor platforms has provided the great advantages for desired result achievement. This paper proposes the use of both mobile laser scanning (MLS data and Unmanned Aerial Vehicle (UAV images for 3D model reconstruction. Due to no available exterior orientation parameters for UAV images, the first task is to georeference these images to 3D points. In order to fast and accurate acquire 3D points which are also easy to be found the corresponding locations on UAV images, automated pole extraction from MLS was developed. After georeferencing UAV images, building roofs are acquired from those images and building walls are extracted from MLS data. The roofs and the walls are combined to achieve the complete building models.

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks.

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Zhong, Jiandan; Lei, Tao; Yao, Guangle

2017-11-24

Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed.

Using aerial photography and image analysis to measure changes in giant reed populations

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A study was conducted along the Rio Grande in southwest Texas to evaluate color-infrared aerial photography combined with supervised image analysis to quantify changes in giant reed (Arundo donax L.) populations over a 6-year period. Aerial photographs from 2002 and 2008 of the same seven study site...

A FAST APPROACH FOR STITCHING OF AERIAL IMAGES

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A. Moussa

2016-06-01

Full Text Available The last few years have witnessed an increasing volume of aerial image data because of the extensive improvements of the Unmanned Aerial Vehicles (UAVs. These newly developed UAVs have led to a wide variety of applications. A fast assessment of the achieved coverage and overlap of the acquired images of a UAV flight mission is of great help to save the time and cost of the further steps. A fast automatic stitching of the acquired images can help to visually assess the achieved coverage and overlap during the flight mission. This paper proposes an automatic image stitching approach that creates a single overview stitched image using the acquired images during a UAV flight mission along with a coverage image that represents the count of overlaps between the acquired images. The main challenge of such task is the huge number of images that are typically involved in such scenarios. A short flight mission with image acquisition frequency of one second can capture hundreds to thousands of images. The main focus of the proposed approach is to reduce the processing time of the image stitching procedure by exploiting the initial knowledge about the images positions provided by the navigation sensors. The proposed approach also avoids solving for all the transformation parameters of all the photos together to save the expected long computation time if all the parameters were considered simultaneously. After extracting the points of interest of all the involved images using Scale-Invariant Feature Transform (SIFT algorithm, the proposed approach uses the initial image’s coordinates to build an incremental constrained Delaunay triangulation that represents the neighborhood of each image. This triangulation helps to match only the neighbor images and therefore reduces the time-consuming features matching step. The estimated relative orientation between the matched images is used to find a candidate seed image for the stitching process. The pre

Siamese-GAN: Learning Invariant Representations for Aerial Vehicle Image Categorization

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Laila Bashmal

2018-02-01

Full Text Available In this paper, we present a new algorithm for cross-domain classification in aerial vehicle images based on generative adversarial networks (GANs. The proposed method, called Siamese-GAN, learns invariant feature representations for both labeled and unlabeled images coming from two different domains. To this end, we train in an adversarial manner a Siamese encoder–decoder architecture coupled with a discriminator network. The encoder–decoder network has the task of matching the distributions of both domains in a shared space regularized by the reconstruction ability, while the discriminator seeks to distinguish between them. After this phase, we feed the resulting encoded labeled and unlabeled features to another network composed of two fully-connected layers for training and classification, respectively. Experiments on several cross-domain datasets composed of extremely high resolution (EHR images acquired by manned/unmanned aerial vehicles (MAV/UAV over the cities of Vaihingen, Toronto, Potsdam, and Trento are reported and discussed.

Assessing the performance of aerial image point cloud and spectral metrics in predicting boreal forest canopy cover

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Melin, M.; Korhonen, L.; Kukkonen, M.; Packalen, P.

2017-07-01

Canopy cover (CC) is a variable used to describe the status of forests and forested habitats, but also the variable used primarily to define what counts as a forest. The estimation of CC has relied heavily on remote sensing with past studies focusing on satellite imagery as well as Airborne Laser Scanning (ALS) using light detection and ranging (lidar). Of these, ALS has been proven highly accurate, because the fraction of pulses penetrating the canopy represents a direct measurement of canopy gap percentage. However, the methods of photogrammetry can be applied to produce point clouds fairly similar to airborne lidar data from aerial images. Currently there is little information about how well such point clouds measure canopy density and gaps. The aim of this study was to assess the suitability of aerial image point clouds for CC estimation and compare the results with those obtained using spectral data from aerial images and Landsat 5. First, we modeled CC for n = 1149 lidar plots using field-measured CCs and lidar data. Next, this data was split into five subsets in north-south direction (y-coordinate). Finally, four CC models (AerialSpectral, AerialPointcloud, AerialCombi (spectral + pointcloud) and Landsat) were created and they were used to predict new CC values to the lidar plots, subset by subset, using five-fold cross validation. The Landsat and AerialSpectral models performed with RMSEs of 13.8% and 12.4%, respectively. AerialPointcloud model reached an RMSE of 10.3%, which was further improved by the inclusion of spectral data; RMSE of the AerialCombi model was 9.3%. We noticed that the aerial image point clouds managed to describe only the outermost layer of the canopy and missed the details in lower canopy, which was resulted in weak characterization of the total CC variation, especially in the tails of the data.

Application of machine learning for the evaluation of turfgrass plots using aerial images

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Ding, Ke; Raheja, Amar; Bhandari, Subodh; Green, Robert L.

2016-05-01

Historically, investigation of turfgrass characteristics have been limited to visual ratings. Although relevant information may result from such evaluations, final inferences may be questionable because of the subjective nature in which the data is collected. Recent advances in computer vision techniques allow researchers to objectively measure turfgrass characteristics such as percent ground cover, turf color, and turf quality from the digital images. This paper focuses on developing a methodology for automated assessment of turfgrass quality from aerial images. Images of several turfgrass plots of varying quality were gathered using a camera mounted on an unmanned aerial vehicle. The quality of these plots were also evaluated based on visual ratings. The goal was to use the aerial images to generate quality evaluations on a regular basis for the optimization of water treatment. Aerial images are used to train a neural network so that appropriate features such as intensity, color, and texture of the turfgrass are extracted from these images. Neural network is a nonlinear classifier commonly used in machine learning. The output of the neural network trained model is the ratings of the grass, which is compared to the visual ratings. Currently, the quality and the color of turfgrass, measured as the greenness of the grass, are evaluated. The textures are calculated using the Gabor filter and co-occurrence matrix. Other classifiers such as support vector machines and simpler linear regression models such as Ridge regression and LARS regression are also used. The performance of each model is compared. The results show encouraging potential for using machine learning techniques for the evaluation of turfgrass quality and color.

Use of Aerial Hyperspectral Imaging For Monitoring Forest Health

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Milton O. Smith; Nolan J. Hess; Stephen Gulick; Lori G. Eckhardt; Roger D. Menard

2004-01-01

This project evaluates the effectiveness of aerial hyperspectral digital imagery in the assessment of forest health of loblolly stands in central Alabama. The imagery covers 50 square miles, in Bibb and Hale Counties, south of Tuscaloosa, AL, which includes intensive managed forest industry sites and National Forest lands with multiple use objectives. Loblolly stands...

UAV remote sensing atmospheric degradation image restoration based on multiple scattering APSF estimation

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Qiu, Xiang; Dai, Ming; Yin, Chuan-li

2017-09-01

Unmanned aerial vehicle (UAV) remote imaging is affected by the bad weather, and the obtained images have the disadvantages of low contrast, complex texture and blurring. In this paper, we propose a blind deconvolution model based on multiple scattering atmosphere point spread function (APSF) estimation to recovery the remote sensing image. According to Narasimhan analytical theory, a new multiple scattering restoration model is established based on the improved dichromatic model. Then using the L0 norm sparse priors of gradient and dark channel to estimate APSF blur kernel, the fast Fourier transform is used to recover the original clear image by Wiener filtering. By comparing with other state-of-the-art methods, the proposed method can correctly estimate blur kernel, effectively remove the atmospheric degradation phenomena, preserve image detail information and increase the quality evaluation indexes.

Detection of Aspens Using High Resolution Aerial Laser Scanning Data and Digital Aerial Images

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Kalle Eerikäinen

2008-08-01

Full Text Available The aim was to use high resolution Aerial Laser Scanning (ALS data and aerial images to detect European aspen (Populus tremula L. from among other deciduous trees. The field data consisted of 14 sample plots of 30 m × 30 m size located in the Koli National Park in the North Karelia, Eastern Finland. A Canopy Height Model (CHM was interpolated from the ALS data with a pulse density of 3.86/m2, low-pass filtered using Height-Based Filtering (HBF and binarized to create the mask needed to separate the ground pixels from the canopy pixels within individual areas. Watershed segmentation was applied to the low-pass filtered CHM in order to create preliminary canopy segments, from which the non-canopy elements were extracted to obtain the final canopy segmentation, i.e. the ground mask was analysed against the canopy mask. A manual classification of aerial images was employed to separate the canopy segments of deciduous trees from those of coniferous trees. Finally, linear discriminant analysis was applied to the correctly classified canopy segments of deciduous trees to classify them into segments belonging to aspen and those belonging to other deciduous trees. The independent variables used in the classification were obtained from the first pulse ALS point data. The accuracy of discrimination between aspen and other deciduous trees was 78.6%. The independent variables in the classification function were the proportion of vegetation hits, the standard deviation of in pulse heights, accumulated intensity at the 90th percentile and the proportion of laser points reflected at the 60th height percentile. The accuracy of classification corresponded to the validation results of earlier ALS-based studies on the classification of individual deciduous trees to tree species.

Augmenting camera images for operators of Unmanned Aerial Vehicles

NARCIS (Netherlands)

Veltman, J.A.; Oving, A.B.

2003-01-01

The manual control of the camera of an unmanned aerial vehicle (UAV) can be difficult due to several factors such as 1) time delays between steering input and changes of the monitor content, 2) low update rates of the camera images and 3) lack of situation awareness due to the remote position of the

A Backward Pyramid Oriented Optical Flow Field Computing Method for Aerial Image

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LI Jiatian

2016-09-01

Full Text Available Aerial image optical flow field is the foundation for detecting moving objects at low altitude and obtaining change information. In general,the image pyramid structure is embedded in numerical procedure in order to enhance the convergence globally. However,more often than not,the pyramid structure is constructed using a bottom-up approach progressively,ignoring the geometry imaging process.In particular,when the ground objects moving it will lead to miss optical flow or the optical flow too small that could hardly sustain the subsequent modeling and analyzing issues. So a backward pyramid structure is proposed on the foundation of top-level standard image. Firstly,down sampled factors of top-level image are calculated quantitatively through central projection,which making the optical flow in top-level image represent the shifting threshold of the set ground target. Secondly,combining top-level image with its original,the down sampled factors in middle layer are confirmed in a constant proportion way. Finally,the image of middle layer is achieved by Gaussian smoothing and image interpolation,and meanwhile the pyramid is formed. The comparative experiments and analysis illustrate that the backward pyramid can calculate the optic flow field in aerial image accurately,and it has advantages in restraining small ground displacement.

Registration of airborne LiDAR data and aerial images based on straight lines and POS data

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Du, Quanye; Xu, Biao; Cao, Hui

2009-10-01

This paper presents a registration method which based on straight lines primitive. Firstly, 2D straight lines are extracted from aerial images using Canny operator and straight line fitting. In the similar way, 3D straight lines are extracted from LiDAR range images which derive from laser scanning point cloud. Secondly, 3D straight lines are projected to aerial images using collinearity equations and Position and Orientation System (POS) data. Then the corresponding lines are determined by straight line error. At last, each image's new exterior orientation elements are calculated by generalized point (straight line) photogrammetry.

Evaluation of Color Settings in Aerial Images with the Use of Eye-Tracking User Study

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Mirijovsky, J.; Popelka, S.

2016-06-01

The main aim of presented paper is to find the most realistic and preferred color settings for four different types of surfaces on the aerial images. This will be achieved through user study with the use of eye-movement recording. Aerial images taken by the unmanned aerial system were used as stimuli. From each image, squared crop area containing one of the studied types of surfaces (asphalt, concrete, water, soil, and grass) was selected. For each type of surface, the real value of reflectance was found with the use of precise spectroradiometer ASD HandHeld 2 which measures the reflectance. The device was used at the same time as aerial images were captured, so lighting conditions and state of vegetation were equal. The spectral resolution of the ASD device is better than 3.0 nm. For defining the RGB values of selected type of surface, the spectral reflectance values recorded by the device were merged into wider groups. Finally, we get three groups corresponding to RGB color system. Captured images were edited with the graphic editor Photoshop CS6. Contrast, clarity, and brightness were edited for all surface types on images. Finally, we get a set of 12 images of the same area with different color settings. These images were put into the grid and used as stimuli for the eye-tracking experiment. Eye-tracking is one of the methods of usability studies and it is considered as relatively objective. Eye-tracker SMI RED 250 with the sampling frequency 250 Hz was used in the study. As respondents, a group of 24 students of Geoinformatics and Geography was used. Their task was to select which image in the grid has the best color settings. The next task was to select which color settings they prefer. Respondents' answers were evaluated and the most realistic and most preferable color settings were found. The advantage of the eye-tracking evaluation was that also the process of the selection of the answers was analyzed. Areas of Interest were marked around each image in the

Complex Building Detection Through Integrating LIDAR and Aerial Photos

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Zhai, R.

2015-02-01

This paper proposes a new approach on digital building detection through the integration of LiDAR data and aerial imagery. It is known that most building rooftops are represented by different regions from different seed pixels. Considering the principals of image segmentation, this paper employs a new region based technique to segment images, combining both the advantages of LiDAR and aerial images together. First, multiple seed points are selected by taking several constraints into consideration in an automated way. Then, the region growing procedures proceed by combining the elevation attribute from LiDAR data, visibility attribute from DEM (Digital Elevation Model), and radiometric attribute from warped images in the segmentation. Through this combination, the pixels with similar height, visibility, and spectral attributes are merged into one region, which are believed to represent the whole building area. The proposed methodology was implemented on real data and competitive results were achieved.

FITTING OF PARAMETRIC BUILDING MODELS TO OBLIQUE AERIAL IMAGES

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U. S. Panday

2012-09-01

Full Text Available In literature and in photogrammetric workstations many approaches and systems to automatically reconstruct buildings from remote sensing data are described and available. Those building models are being used for instance in city modeling or in cadastre context. If a roof overhang is present, the building walls cannot be estimated correctly from nadir-view aerial images or airborne laser scanning (ALS data. This leads to inconsistent building outlines, which has a negative influence on visual impression, but more seriously also represents a wrong legal boundary in the cadaster. Oblique aerial images as opposed to nadir-view images reveal greater detail, enabling to see different views of an object taken from different directions. Building walls are visible from oblique images directly and those images are used for automated roof overhang estimation in this research. A fitting algorithm is employed to find roof parameters of simple buildings. It uses a least squares algorithm to fit projected wire frames to their corresponding edge lines extracted from the images. Self-occlusion is detected based on intersection result of viewing ray and the planes formed by the building whereas occlusion from other objects is detected using an ALS point cloud. Overhang and ground height are obtained by sweeping vertical and horizontal planes respectively. Experimental results are verified with high resolution ortho-images, field survey, and ALS data. Planimetric accuracy of 1cm mean and 5cm standard deviation was obtained, while buildings' orientation were accurate to mean of 0.23° and standard deviation of 0.96° with ortho-image. Overhang parameters were aligned to approximately 10cm with field survey. The ground and roof heights were accurate to mean of – 9cm and 8cm with standard deviations of 16cm and 8cm with ALS respectively. The developed approach reconstructs 3D building models well in cases of sufficient texture. More images should be acquired for

Neural-network classifiers for automatic real-world aerial image recognition

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Greenberg, Shlomo; Guterman, Hugo

1996-08-01

We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.

Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning

Science.gov (United States)

Vetrivel, Anand; Gerke, Markus; Kerle, Norman; Nex, Francesco; Vosselman, George

2018-06-01

Oblique aerial images offer views of both building roofs and façades, and thus have been recognized as a potential source to detect severe building damages caused by destructive disaster events such as earthquakes. Therefore, they represent an important source of information for first responders or other stakeholders involved in the post-disaster response process. Several automated methods based on supervised learning have already been demonstrated for damage detection using oblique airborne images. However, they often do not generalize well when data from new unseen sites need to be processed, hampering their practical use. Reasons for this limitation include image and scene characteristics, though the most prominent one relates to the image features being used for training the classifier. Recently features based on deep learning approaches, such as convolutional neural networks (CNNs), have been shown to be more effective than conventional hand-crafted features, and have become the state-of-the-art in many domains, including remote sensing. Moreover, often oblique images are captured with high block overlap, facilitating the generation of dense 3D point clouds - an ideal source to derive geometric characteristics. We hypothesized that the use of CNN features, either independently or in combination with 3D point cloud features, would yield improved performance in damage detection. To this end we used CNN and 3D features, both independently and in combination, using images from manned and unmanned aerial platforms over several geographic locations that vary significantly in terms of image and scene characteristics. A multiple-kernel-learning framework, an effective way for integrating features from different modalities, was used for combining the two sets of features for classification. The results are encouraging: while CNN features produced an average classification accuracy of about 91%, the integration of 3D point cloud features led to an additional

Scheduling System for Multiple Unmanned Aerial Vehicles in Indoor Environments Using the CSP Approach

DEFF Research Database (Denmark)

Park, Youngsoo; Khosiawan, Yohanes; Moon, Ilkyeong

2016-01-01

In recent years there has been an increased demand in use of multiple unmanned aerial vehicles (UAVs) for surveillance and material handling tasks in indoor environments. However, only a limited number of studies have been reported on UAV scheduling in an indoor 3D environment. This paper present...

Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations.

Science.gov (United States)

Huang, Rongyong; Zheng, Shunyi; Hu, Kun

2018-06-01

Registration of large-scale optical images with airborne LiDAR data is the basis of the integration of photogrammetry and LiDAR. However, geometric misalignments still exist between some aerial optical images and airborne LiDAR point clouds. To eliminate such misalignments, we extended a method for registering close-range optical images with terrestrial LiDAR data to a variety of large-scale aerial optical images and airborne LiDAR data. The fundamental principle is to minimize the distances from the photogrammetric matching points to the terrestrial LiDAR data surface. Except for the satisfactory efficiency of about 79 s per 6732 × 8984 image, the experimental results also show that the unit weighted root mean square (RMS) of the image points is able to reach a sub-pixel level (0.45 to 0.62 pixel), and the actual horizontal and vertical accuracy can be greatly improved to a high level of 1/4⁻1/2 (0.17⁻0.27 m) and 1/8⁻1/4 (0.10⁻0.15 m) of the average LiDAR point distance respectively. Finally, the method is proved to be more accurate, feasible, efficient, and practical in variety of large-scale aerial optical image and LiDAR data.

Comparisons of feature extraction algorithm based on unmanned aerial vehicle image

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Xi Wenfei

2017-07-01

Full Text Available Feature point extraction technology has become a research hotspot in the photogrammetry and computer vision. The commonly used point feature extraction operators are SIFT operator, Forstner operator, Harris operator and Moravec operator, etc. With the high spatial resolution characteristics, UAV image is different from the traditional aviation image. Based on these characteristics of the unmanned aerial vehicle (UAV, this paper uses several operators referred above to extract feature points from the building images, grassland images, shrubbery images, and vegetable greenhouses images. Through the practical case analysis, the performance, advantages, disadvantages and adaptability of each algorithm are compared and analyzed by considering their speed and accuracy. Finally, the suggestions of how to adapt different algorithms in diverse environment are proposed.

NEAR REAL-TIME GEOREFERENCE OF UMANNED AERIAL VEHICLE IMAGES FOR POST-EARTHQUAKE RESPONSE

OpenAIRE

Wang, S.; Wang, X.; Dou, A.; Yuan, X.; Ding, L.; Ding, X.

2018-01-01

The rapid collection of Unmanned Aerial Vehicle (UAV) remote sensing images plays an important role in the fast submitting disaster information and the monitored serious damaged objects after the earthquake. However, for hundreds of UAV images collected in one flight sortie, the traditional data processing methods are image stitching and three-dimensional reconstruction, which take one to several hours, and affect the speed of disaster response. If the manual searching method is employed, we ...

Registration of Urban Aerial Image and LiDAR Based on Line Vectors

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Qinghong Sheng

2017-09-01

Full Text Available In a traditional registration of a single aerial image with airborne light detection and ranging (LiDAR data using linear features that regard line direction as a control or linear features as constraints in the solution, lacking the constraint of linear position leads to the error propagation of the adjustment model. To solve this problem, this paper presents a line vector-based registration mode (LVR in which image rays and LiDAR lines are expressed by a line vector that integrates the line direction and the line position. A registration equation of line vector is set up by coplanar imaging rays and corresponding control lines. Three types of datasets consisting of synthetic, theInternational Society for Photogrammetry and Remote Sensing (ISPRS test project, and real aerial data are used. A group of progressive experiments is undertaken to evaluate the robustness of the LVR. Experimental results demonstrate that the integrated line direction and the line position contributes a great deal to the theoretical and real accuracies of the unknowns, as well as the stability of the adjustment model. This paper provides a new suggestion that, for a single image and LiDAR data, registration in urban areas can be accomplished by accommodating rich line features.

Generalized internal multiple imaging

KAUST Repository

Zuberi, Mohammad Akbar Hosain

2014-12-04

Various examples are provided for generalized internal multiple imaging (GIMI). In one example, among others, a method includes generating a higher order internal multiple image using a background Green\\'s function and rendering the higher order internal multiple image for presentation. In another example, a system includes a computing device and a generalized internal multiple imaging (GIMI) application executable in the computing device. The GIMI application includes logic that generates a higher order internal multiple image using a background Green\\'s function and logic that renders the higher order internal multiple image for display on a display device. In another example, a non-transitory computer readable medium has a program executable by processing circuitry that generates a higher order internal multiple image using a background Green\\'s function and renders the higher order internal multiple image for display on a display device.

Generalized internal multiple imaging

KAUST Repository

Zuberi, Mohammad Akbar Hosain; Alkhalifah, Tariq

2014-01-01

Various examples are provided for generalized internal multiple imaging (GIMI). In one example, among others, a method includes generating a higher order internal multiple image using a background Green's function and rendering the higher order internal multiple image for presentation. In another example, a system includes a computing device and a generalized internal multiple imaging (GIMI) application executable in the computing device. The GIMI application includes logic that generates a higher order internal multiple image using a background Green's function and logic that renders the higher order internal multiple image for display on a display device. In another example, a non-transitory computer readable medium has a program executable by processing circuitry that generates a higher order internal multiple image using a background Green's function and renders the higher order internal multiple image for display on a display device.

ORIENTATION AND DENSE RECONSTRUCTION OF UNORDERED TERRESTRIAL AND AERIAL WIDE BASELINE IMAGE SETS

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J. Bartelsen

2012-07-01

Full Text Available In this paper we present an approach for detailed and precise automatic dense 3D reconstruction using images from consumer cameras. The major difference between our approach and many others is that we focus on wide-baseline image sets. We have combined and improved several methods, particularly, least squares matching, RANSAC, scale-space maxima and bundle adjustment, for robust matching and parameter estimation. Point correspondences and the five-point algorithm lead to relative orientation. Due to our robust matching method it is possible to orient images under much more unfavorable conditions, for instance concerning illumination changes or scale differences, than for often used operators such as SIFT. For dense reconstruction, we use our orientation as input for Semiglobal Matching (SGM resulting into dense depth images. The latter can be fused into a 2.5D model for eliminating the redundancy of the highly overlapping depth images. However, some applications require full 3D models. A solution to this problem is part of our current work, for which preliminary results are presented in this paper. With very small unmanned aerial systems (Micro UAS it is possible to acquire images which have a perspective similar to terrestrial images and can thus be combined with them. Such a combination is useful for an almost complete 3D reconstruction of urban scenes. We have applied our approach to several hundred aerial and terrestrial images and have generated detailed 2.5D and 3D models of urban areas.

Extracting Buildings from True Color Stereo Aerial Images Using a Decision Making Strategy

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Eufemia Tarantino

2011-07-01

Full Text Available The automatic extraction of buildings from true color stereo aerial imagery in a dense built-up area is the main focus of this paper. Our approach strategy aimed at reducing the complexity of the image content by means of a three-step procedure combining reliable geospatial image analysis techniques. Even if it is a rudimentary first step towards a more general approach, the method presented proved useful in urban sprawl studies for rapid map production in flat area by retrieving indispensable information on buildings from scanned historic aerial photography. After the preliminary creation of a photogrammetric model to manage Digital Surface Model and orthophotos, five intermediate mask-layers data (Elevation, Slope, Vegetation, Shadow, Canny, Shadow, Edges were processed through the combined use of remote sensing image processing and GIS software environments. Lastly, a rectangular building block model without roof structures (Level of Detail, LoD1 was automatically generated. System performance was evaluated with objective criteria, showing good results in a complex urban area featuring various types of building objects.

Road Extraction and Car Detection from Aerial Image Using Intensity and Color

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Vahid Ghods

2011-07-01

Full Text Available In this paper a new automatic approach to road extraction from aerial images is proposed. The initialization strategies are based on the intensity, color, and Hough transform. After road elements extraction, chain codes are calculated. In the last step, using shadow, cars on the roads are detected. We implemented our method on the 25 images from "Google Earth" database. The experiments show an increase in both the completeness and the quality indexes for the extracted road.

First demonstration of aerial gamma-ray imaging using drone for prompt radiation survey in Fukushima

Science.gov (United States)

Mochizuki, S.; Kataoka, J.; Tagawa, L.; Iwamoto, Y.; Okochi, H.; Katsumi, N.; Kinno, S.; Arimoto, M.; Maruhashi, T.; Fujieda, K.; Kurihara, T.; Ohsuka, S.

2017-11-01

Considerable amounts of radioactive substances (mainly 137Cs and 134Cs) were released into the environment after the Japanese nuclear disaster in 2011. Some restrictions on residence areas were lifted in April 2017, owing to the successive and effective decontamination operations. However, the distribution of radioactive substances in vast areas of mountain, forest and satoyama close to the city is still unknown; thus, decontamination operations in such areas are being hampered. In this paper, we report on the first aerial gamma-ray imaging of a schoolyard in Fukushima using a drone that carries a high sensitivity Compton camera. We show that the distribution of 137Cs in regions with a diameter of several tens to a hundred meters can be imaged with a typical resolution of 2-5 m within a 10-20 min flights duration. The aerial gamma-ray images taken 10 m and 20 m above the ground are qualitatively consistent with a dose map reconstructed from the ground-based measurements using a survey meter. Although further quantification is needed for the distance and air-absorption corrections to derive in situ dose map, such an aerial drone system can reduce measurement time by a factor of ten and is suitable for place where ground-based measurement are difficult.

Looking for an old aerial photograph

Science.gov (United States)

,

1997-01-01

Attempts to photograph the surface of the Earth date from the 1800's, when photographers attached cameras to balloons, kites, and even pigeons. Today, aerial photographs and satellite images are commonplace. The rate of acquiring aerial photographs and satellite images has increased rapidly in recent years. Views of the Earth obtained from aircraft or satellites have become valuable tools to Government resource planners and managers, land-use experts, environmentalists, engineers, scientists, and a wide variety of other users. Many people want historical aerial photographs for business or personal reasons. They may want to locate the boundaries of an old farm or a piece of family property. Or they may want a photograph as a record of changes in their neighborhood, or as a gift. The U.S. Geological Survey (USGS) maintains the Earth Science Information Centers (ESIC?s) to sell aerial photographs, remotely sensed images from satellites, a wide array of digital geographic and cartographic data, as well as the Bureau?s wellknown maps. Declassified photographs from early spy satellites were recently added to the ESIC offerings of historical images. Using the Aerial Photography Summary Record System database, ESIC researchers can help customers find imagery in the collections of other Federal agencies and, in some cases, those of private companies that specialize in esoteric products.

Integration of aerial imaging and variable-rate technology for site-specific aerial herbicide application

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As remote sensing and variable rate technology are becoming more available for aerial applicators, practical methodologies on effective integration of these technologies are needed for site-specific aerial applications of crop production and protection materials. The objectives of this study were to...

Employing Multiple Unmanned Aerial Vehicles for Co-Operative Path Planning

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Durdana Habib

2013-05-01

Full Text Available Abstract In this paper, we work to develop a path planning solution for a group of Unmanned Aerial Vehicles (UAVs using a Mixed Integer Linear Programming (MILP approach. Co-operation among team members not only helps reduce mission time, it makes the execution more robust in dynamic environments. However, the problem becomes more challenging as it requires optimal resource allocation and is NP-hard. Since UAVs may be lost or may suffer significant damage during the course of the mission, plans may need to be modified in real-time as the mission proceeds. Therefore, multiple UAVs have a better chance of completing a mission in the face of failures. Such military operations can be treated as a variant of the Multiple Depot Vehicle Routing Problem (MDVRP. The proposed solution must be such that m UAVs start from multiple source locations to visit n targets and return to a set of destination locations such that (1 each target is visited exactly by one of the chosen UAVs (2 the total distance travelled by the group is minimized and (3 the number of targets that each UAV visits may not be less than K or greater than L.

Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake

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Shuai Xie

2016-09-01

Full Text Available Remote sensing (RS images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse assessment in the early time after an earthquake based on aerial remote sensing image. The procedure of RS data pre-processing and crowdsourcing data collection is presented. A probabilistic model including maximum likelihood estimation (MLE, Bayes’ theorem and expectation-maximization (EM algorithm are applied to quantitatively estimate the individual error-rate and “ground truth” according to multiple participants’ assessment results. An experimental area of Yushu earthquake is provided to present the results contributed by participants. Following the results, some discussion is provided regarding accuracy and variation among participants. The features of buildings labeled as the same damage type are found highly consistent. This suggests that the building damage assessment contributed by crowdsourcing can be treated as reliable samples. This study shows potential for a rapid building collapse assessment through crowdsourcing and quantitatively inferring “ground truth” according to crowdsourcing data in the early time after the earthquake based on aerial remote sensing image.

Generalized internal multiple imaging

KAUST Repository

Zuberi, M. A. H.

2014-08-05

Internal multiples deteriorate the image when the imaging procedure assumes only single scattering, especially if the velocity model does not have sharp contrasts to reproduce such scattering in the Green’s function through forward modeling. If properly imaged, internal multiples (internally scattered energy) can enhance the seismic image. Conventionally, to image internal multiples, accurate, sharp contrasts in the velocity model are required to construct a Green’s function with all the scattered energy. As an alternative, we have developed a generalized internal multiple imaging procedure that images any order internal scattering using the background Green’s function (from the surface to each image point), constructed from a smooth velocity model, usually used for conventional imaging. For the first-order internal multiples, the approach consisted of three steps, in which we first back propagated the recorded surface seismic data using the background Green’s function, then crosscorrelated the back-propagated data with the recorded data, and finally crosscorrelated the result with the original background Green’s function. This procedure images the contribution of the recorded first-order internal multiples, and it is almost free of the single-scattering recorded energy. The cost includes one additional crosscorrelation over the conventional single-scattering imaging application. We generalized this method to image internal multiples of any order separately. The resulting images can be added to the conventional single-scattering image, obtained, e.g., from Kirchhoff or reverse-time migration, to enhance the image. Application to synthetic data with reflectors illuminated by multiple scattering (double scattering) demonstrated the effectiveness of the approach.

Direct Penguin Counting Using Unmanned Aerial Vehicle Image

Science.gov (United States)

Hyun, C. U.; Kim, H. C.; Kim, J. H.; Hong, S. G.

2015-12-01

This study presents an application of unmanned aerial vehicle (UAV) images to monitor penguin colony in Baton Peninsula, King George Island, Antarctica. The area around Narębski Point located on the southeast coast of Barton Peninsula was designated as Antarctic Specially Protected Area No. 171 (ASPA 171), and Chinstrap and Gentoo penguins inhabit in this area. The UAV images were acquired in a part of ASPA 171 from four flights in a single day, Jan 18, 2014. About 360 images were mosaicked as an image of about 3 cm spatial resolution and then a subset including representative penguin rookeries was selected. The subset image was segmented based on gradient map of pixel values, and spectral and spatial attributes were assigned to each segment. The object based image analysis (OBIA) was conducted with consideration of spectral attributes including mean and minimum values of each segment and various shape attributes such as area, length, compactness and roundness to detect individual penguin. The segments indicating individual penguin were effectively detected on rookeries with high contrasts in the spectral and shape attributes. The importance of periodic and precise monitoring of penguins has been recognized because variations of their populations reflect environmental changes and disturbance from human activities. Utilization of very high resolution imaging method shown in this study can be applied to other penguin habitats in Antarctica, and the results will be able to support establishing effective environmental management plans.

COMPREHENSIVE COMPARISON OF TWO IMAGE-BASED POINT CLOUDS FROM AERIAL PHOTOS WITH AIRBORNE LIDAR FOR LARGE-SCALE MAPPING

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E. Widyaningrum

2017-09-01

Full Text Available The integration of computer vision and photogrammetry to generate three-dimensional (3D information from images has contributed to a wider use of point clouds, for mapping purposes. Large-scale topographic map production requires 3D data with high precision and accuracy to represent the real conditions of the earth surface. Apart from LiDAR point clouds, the image-based matching is also believed to have the ability to generate reliable and detailed point clouds from multiple-view images. In order to examine and analyze possible fusion of LiDAR and image-based matching for large-scale detailed mapping purposes, point clouds are generated by Semi Global Matching (SGM and by Structure from Motion (SfM. In order to conduct comprehensive and fair comparison, this study uses aerial photos and LiDAR data that were acquired at the same time. Qualitative and quantitative assessments have been applied to evaluate LiDAR and image-matching point clouds data in terms of visualization, geometric accuracy, and classification result. The comparison results conclude that LiDAR is the best data for large-scale mapping.

FUNCTIONALITY ASSESSMENT OF ALGORITHMS FOR THE COLORING OF IMAGES IN TERMS OF INCREASING RADIOMETRIC VALUES OF AERIAL PHOTOGRAPHS ARCHIVES

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Ewiak Ireneusz

2016-12-01

Full Text Available Available on the commercial market are a number of algorithms that enable assigning to pixels of a monochrome digital image suitable colors according to a strictly defined schedule. These algorithms have been recently used by professional film studios involved in the coloring of archival productions. This article provides an overview on the functionality of coloring algorithms in terms of their use to improve the interpretation quality of historical, black - and - white aerial photographs. The analysis covered intuitive (Recolored programs, as well as more advanced (Adobe After Effect, DaVinci Resolve programs. The use of their full functionality was limited by the too large information capacity of aerial photograph images. Black - and - white historical aerial photographs, which interpretation quality in many cases does not meet the criteria posed on photogrammetric developments, require an increase of their readability. The solution in this regard may be the process of coloring images. The authors of this article conducted studies aimed to determine to what extent the tested coloring algorithms enable an automatic detection of land cover elements on historical aerial photographs and provide color close to the natural. Used in the studies were archival black - and - white aerial photographs of the western part of Warsaw district made available by the Main Centre of Geodetic and Cartographic Documentation , the selection of which was associated with the presence in this area of various elements of land cover, such as water, forests, crops, exposed soils and also anthropogenic objects. In the analysis of different algorithms are included: format and size of the image, degree of automation of the process, degree of compliance of the result and processing time. The accuracy of the coloring process was different for each class of objects mapped on the photograph. The main limitation of the coloring process created shadows of anthropogenic objects

Fast Aerial Video Stitching

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Jing Li

2014-10-01

Full Text Available The highly efficient and robust stitching of aerial video captured by unmanned aerial vehicles (UAVs is a challenging problem in the field of robot vision. Existing commercial image stitching systems have seen success with offline stitching tasks, but they cannot guarantee high-speed performance when dealing with online aerial video sequences. In this paper, we present a novel system which has an unique ability to stitch high-frame rate aerial video at a speed of 150 frames per second (FPS. In addition, rather than using a high-speed vision platform such as FPGA or CUDA, our system is running on a normal personal computer. To achieve this, after the careful comparison of the existing invariant features, we choose the FAST corner and binary descriptor for efficient feature extraction and representation, and present a spatial and temporal coherent filter to fuse the UAV motion information into the feature matching. The proposed filter can remove the majority of feature correspondence outliers and significantly increase the speed of robust feature matching by up to 20 times. To achieve a balance between robustness and efficiency, a dynamic key frame-based stitching framework is used to reduce the accumulation errors. Extensive experiments on challenging UAV datasets demonstrate that our approach can break through the speed limitation and generate an accurate stitching image for aerial video stitching tasks.

AERIAL IMAGES FROM AN UAV SYSTEM: 3D MODELING AND TREE SPECIES CLASSIFICATION IN A PARK AREA

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R. Gini

2012-07-01

Full Text Available The use of aerial imagery acquired by Unmanned Aerial Vehicles (UAVs is scheduled within the FoGLIE project (Fruition of Goods Landscape in Interactive Environment: it starts from the need to enhance the natural, artistic and cultural heritage, to produce a better usability of it by employing audiovisual movable systems of 3D reconstruction and to improve monitoring procedures, by using new media for integrating the fruition phase with the preservation ones. The pilot project focus on a test area, Parco Adda Nord, which encloses various goods' types (small buildings, agricultural fields and different tree species and bushes. Multispectral high resolution images were taken by two digital compact cameras: a Pentax Optio A40 for RGB photos and a Sigma DP1 modified to acquire the NIR band. Then, some tests were performed in order to analyze the UAV images' quality with both photogrammetric and photo-interpretation purposes, to validate the vector-sensor system, the image block geometry and to study the feasibility of tree species classification. Many pre-signalized Control Points were surveyed through GPS to allow accuracy analysis. Aerial Triangulations (ATs were carried out with photogrammetric commercial software, Leica Photogrammetry Suite (LPS and PhotoModeler, with manual or automatic selection of Tie Points, to pick out pros and cons of each package in managing non conventional aerial imagery as well as the differences in the modeling approach. Further analysis were done on the differences between the EO parameters and the corresponding data coming from the on board UAV navigation system.

Detection of laurel wilt disease in avocado using low altitude aerial imaging.

Science.gov (United States)

de Castro, Ana I; Ehsani, Reza; Ploetz, Randy C; Crane, Jonathan H; Buchanon, Sherrie

2015-01-01

Laurel wilt is a lethal disease of plants in the Lauraceae plant family, including avocado (Persea americana). This devastating disease has spread rapidly along the southeastern seaboard of the United States and has begun to affect commercial avocado production in Florida. The main objective of this study was to evaluate the potential to discriminate laurel wilt-affected avocado trees using aerial images taken with a modified camera during helicopter surveys at low-altitude in the commercial avocado production area. The ability to distinguish laurel wilt-affected trees from other factors that produce similar external symptoms was also studied. RmodGB digital values of healthy trees and laurel wilt-affected trees, as well as fruit stress and vines covering trees were used to calculate several vegetation indices (VIs), band ratios, and VI combinations. These indices were subjected to analysis of variance (ANOVA) and an M-statistic was performed in order to quantify the separability of those classes. Significant differences in spectral values among laurel wilt affected and healthy trees were observed in all vegetation indices calculated, although the best results were achieved with Excess Red (ExR), (Red-Green) and Combination 1 (COMB1) in all locations. B/G showed a very good potential for separate the other factors with symptoms similar to laurel wilt-affected trees, such as fruit stress and vines covering trees, from laurel wilt-affected trees. These consistent results prove the usefulness of using a modified camera (RmodGB) to discriminate laurel wilt-affected avocado trees from healthy trees, as well as from other factors that cause the same symptoms and suggest performing the classification in further research. According to our results, ExR and B/G should be utilized to develop an algorithm or decision rules to classify aerial images, since they showed the highest capacity to discriminate laurel wilt-affected trees. This methodology may allow the rapid detection

Detection of laurel wilt disease in avocado using low altitude aerial imaging.

Directory of Open Access Journals (Sweden)

Ana I de Castro

Full Text Available Laurel wilt is a lethal disease of plants in the Lauraceae plant family, including avocado (Persea americana. This devastating disease has spread rapidly along the southeastern seaboard of the United States and has begun to affect commercial avocado production in Florida. The main objective of this study was to evaluate the potential to discriminate laurel wilt-affected avocado trees using aerial images taken with a modified camera during helicopter surveys at low-altitude in the commercial avocado production area. The ability to distinguish laurel wilt-affected trees from other factors that produce similar external symptoms was also studied. RmodGB digital values of healthy trees and laurel wilt-affected trees, as well as fruit stress and vines covering trees were used to calculate several vegetation indices (VIs, band ratios, and VI combinations. These indices were subjected to analysis of variance (ANOVA and an M-statistic was performed in order to quantify the separability of those classes. Significant differences in spectral values among laurel wilt affected and healthy trees were observed in all vegetation indices calculated, although the best results were achieved with Excess Red (ExR, (Red-Green and Combination 1 (COMB1 in all locations. B/G showed a very good potential for separate the other factors with symptoms similar to laurel wilt-affected trees, such as fruit stress and vines covering trees, from laurel wilt-affected trees. These consistent results prove the usefulness of using a modified camera (RmodGB to discriminate laurel wilt-affected avocado trees from healthy trees, as well as from other factors that cause the same symptoms and suggest performing the classification in further research. According to our results, ExR and B/G should be utilized to develop an algorithm or decision rules to classify aerial images, since they showed the highest capacity to discriminate laurel wilt-affected trees. This methodology may allow the

Algorithm for automatic image dodging of unmanned aerial vehicle images using two-dimensional radiometric spatial attributes

Science.gov (United States)

Li, Wenzhuo; Sun, Kaimin; Li, Deren; Bai, Ting

2016-07-01

Unmanned aerial vehicle (UAV) remote sensing technology has come into wide use in recent years. The poor stability of the UAV platform, however, produces more inconsistencies in hue and illumination among UAV images than other more stable platforms. Image dodging is a process used to reduce these inconsistencies caused by different imaging conditions. We propose an algorithm for automatic image dodging of UAV images using two-dimensional radiometric spatial attributes. We use object-level image smoothing to smooth foreground objects in images and acquire an overall reference background image by relative radiometric correction. We apply the Contourlet transform to separate high- and low-frequency sections for every single image, and replace the low-frequency section with the low-frequency section extracted from the corresponding region in the overall reference background image. We apply the inverse Contourlet transform to reconstruct the final dodged images. In this process, a single image must be split into reasonable block sizes with overlaps due to large pixel size. Experimental mosaic results show that our proposed method reduces the uneven distribution of hue and illumination. Moreover, it effectively eliminates dark-bright interstrip effects caused by shadows and vignetting in UAV images while maximally protecting image texture information.

Small unmanned aerial vehicles (micro-UAVs, drones) in plant ecology.

Science.gov (United States)

Cruzan, Mitchell B; Weinstein, Ben G; Grasty, Monica R; Kohrn, Brendan F; Hendrickson, Elizabeth C; Arredondo, Tina M; Thompson, Pamela G

2016-09-01

Low-elevation surveys with small aerial drones (micro-unmanned aerial vehicles [UAVs]) may be used for a wide variety of applications in plant ecology, including mapping vegetation over small- to medium-sized regions. We provide an overview of methods and procedures for conducting surveys and illustrate some of these applications. Aerial images were obtained by flying a small drone along transects over the area of interest. Images were used to create a composite image (orthomosaic) and a digital surface model (DSM). Vegetation classification was conducted manually and using an automated routine. Coverage of an individual species was estimated from aerial images. We created a vegetation map for the entire region from the orthomosaic and DSM, and mapped the density of one species. Comparison of our manual and automated habitat classification confirmed that our mapping methods were accurate. A species with high contrast to the background matrix allowed adequate estimate of its coverage. The example surveys demonstrate that small aerial drones are capable of gathering large amounts of information on the distribution of vegetation and individual species with minimal impact to sensitive habitats. Low-elevation aerial surveys have potential for a wide range of applications in plant ecology.

Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images.

Science.gov (United States)

Pang, Shiyan; Hu, Xiangyun; Cai, Zhongliang; Gong, Jinqi; Zhang, Mi

2018-03-24

In this work, a novel building change detection method from bi-temporal dense-matching point clouds and aerial images is proposed to address two major problems, namely, the robust acquisition of the changed objects above ground and the automatic classification of changed objects into buildings or non-buildings. For the acquisition of changed objects above ground, the change detection problem is converted into a binary classification, in which the changed area above ground is regarded as the foreground and the other area as the background. For the gridded points of each period, the graph cuts algorithm is adopted to classify the points into foreground and background, followed by the region-growing algorithm to form candidate changed building objects. A novel structural feature that was extracted from aerial images is constructed to classify the candidate changed building objects into buildings and non-buildings. The changed building objects are further classified as "newly built", "taller", "demolished", and "lower" by combining the classification and the digital surface models of two periods. Finally, three typical areas from a large dataset are used to validate the proposed method. Numerous experiments demonstrate the effectiveness of the proposed algorithm.

ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES

Directory of Open Access Journals (Sweden)

Y.-T. Hsieh

2016-06-01

Full Text Available The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i The DN values in shadow area are much lower than in nonshadow area; (ii DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv The shadow area NIR of vegetation category also shows a strong reflection; (v Generally, vegetation indexes (NDVI still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40 is potential for the extract land cover information of shadow areas.

Some results on the investigation of earth resources by aerial and polygon methods

Energy Technology Data Exchange (ETDEWEB)

Vinnichenko, N K; Tishchenko, A P

1980-01-01

Papers are presented on integrated aerial-satellite remote sensing systems, the resolution of TV scanning systems, the transfer of spectral contrasts in multispectral photography, and pseudocolor representation of multispectral aerial images. Consideration is also given to the use of spectral and physical-geographic characteristics of natural objects on the earth's surface for the interpretation of multispectral satellite photographs, the determination of the types and state of crops from multispectral aerial images, and the automated classification of agricultural objects from their multispectral aerial images.

Auditory decision aiding in supervisory control of multiple unmanned aerial vehicles.

Science.gov (United States)

Donmez, Birsen; Cummings, M L; Graham, Hudson D

2009-10-01

This article is an investigation of the effectiveness of sonifications, which are continuous auditory alerts mapped to the state of a monitored task, in supporting unmanned aerial vehicle (UAV) supervisory control. UAV supervisory control requires monitoring a UAV across multiple tasks (e.g., course maintenance) via a predominantly visual display, which currently is supported with discrete auditory alerts. Sonification has been shown to enhance monitoring performance in domains such as anesthesiology by allowing an operator to immediately determine an entity's (e.g., patient) current and projected states, and is a promising alternative to discrete alerts in UAV control. However, minimal research compares sonification to discrete alerts, and no research assesses the effectiveness of sonification for monitoring multiple entities (e.g., multiple UAVs). The authors conducted an experiment with 39 military personnel, using a simulated setup. Participants controlled single and multiple UAVs and received sonifications or discrete alerts based on UAV course deviations and late target arrivals. Regardless of the number of UAVs supervised, the course deviation sonification resulted in reactions to course deviations that were 1.9 s faster, a 19% enhancement, compared with discrete alerts. However, course deviation sonifications interfered with the effectiveness of discrete late arrival alerts in general and with operator responses to late arrivals when supervising multiple vehicles. Sonifications can outperform discrete alerts when designed to aid operators to predict future states of monitored tasks. However, sonifications may mask other auditory alerts and interfere with other monitoring tasks that require divided attention. This research has implications for supervisory control display design.

Mission control of multiple unmanned aerial vehicles: a workload analysis.

Science.gov (United States)

Dixon, Stephen R; Wickens, Christopher D; Chang, Dervon

2005-01-01

With unmanned aerial vehicles (UAVs), 36 licensed pilots flew both single-UAV and dual-UAV simulated military missions. Pilots were required to navigate each UAV through a series of mission legs in one of the following three conditions: a baseline condition, an auditory autoalert condition, and an autopilot condition. Pilots were responsible for (a) mission completion, (b) target search, and (c) systems monitoring. Results revealed that both the autoalert and the autopilot automation improved overall performance by reducing task interference and alleviating workload. The autoalert system benefited performance both in the automated task and mission completion task, whereas the autopilot system benefited performance in the automated task, the mission completion task, and the target search task. Practical implications for the study include the suggestion that reliable automation can help alleviate task interference and reduce workload, thereby allowing pilots to better handle concurrent tasks during single- and multiple-UAV flight control.

Fuzzy C-Means Algorithm for Segmentation of Aerial Photography Data Obtained Using Unmanned Aerial Vehicle

Science.gov (United States)

Akinin, M. V.; Akinina, N. V.; Klochkov, A. Y.; Nikiforov, M. B.; Sokolova, A. V.

2015-05-01

The report reviewed the algorithm fuzzy c-means, performs image segmentation, give an estimate of the quality of his work on the criterion of Xie-Beni, contain the results of experimental studies of the algorithm in the context of solving the problem of drawing up detailed two-dimensional maps with the use of unmanned aerial vehicles. According to the results of the experiment concluded that the possibility of applying the algorithm in problems of decoding images obtained as a result of aerial photography. The considered algorithm can significantly break the original image into a plurality of segments (clusters) in a relatively short period of time, which is achieved by modification of the original k-means algorithm to work in a fuzzy task.

Issues in bridge deck damage evaluation using aerial photos

Science.gov (United States)

Natarajan, M.; Chen, S. E.; Boyle, C.; Martin, E.; Hauser, E.

2012-04-01

Small format aerial photography (SFAP) with low flying technique is proposed for damage evaluation of bridge decks. High resolution images obtained using under-belly photography can be used to quantify the various bridge deck problems. The conventional truck-mount or vehicle-mount deck imaging technologies require a large number of image samples. Hence the physical scanning is time consuming and it is also challenging consider the size and location of a bridge. Aerial imaging overcomes these issues, but they face different kinds of challenges that are posed by obstacles such as shadow from trees, power lines and vehicles, signs and luminaries structures. The image resolution uncertainty, which is a function of the pilot skills and flying conditions, may also add additional challenges to aerial imaging technique. Hence different image processing tools have to be integrated into a single package to achieve the desired task. This paper summarizes the challenges faced and the preliminary results are presented and discussed.

Counter Unmanned Aerial Systems Testing: Evaluation of VIS SWIR MWIR and LWIR passive imagers.

Energy Technology Data Exchange (ETDEWEB)

Birch, Gabriel Carlisle [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Woo, Bryana Lynn [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-01-01

This report contains analysis of unmanned aerial systems as imaged by visible, short-wave infrared, mid-wave infrared, and long-wave infrared passive devices. Testing was conducted at the Nevada National Security Site (NNSS) during the week of August 15, 2016. Target images in all spectral bands are shown and contrast versus background is reported. Calculations are performed to determine estimated pixels-on-target for detection and assessment levels, and the number of pixels needed to cover a hemisphere for detection or assessment at defined distances. Background clutter challenges are qualitatively discussed for different spectral bands, and low contrast scenarios are highlighted for long-wave infrared imagers.

Digital image integration technique of multi-geoscience information dominated by aerial radiometric measurements

International Nuclear Information System (INIS)

Liu Dechang; Sun Maorong; Zhu Deling; Zhang Jingbo; He Jianguo; Dong Xiuzhen

1992-02-01

The geologic metallogenetic environment of uranium at Lian Shan Guan region has been studied by using digital image integration technique of multi-geoscience information with aerial radiometric measurements. It includes the classification of uranium-bearing rock, recognizing patterns of ore-forming and geologic mapping in ore field. Some new tectonic structure was found in this region that gives significant information for further exploring of uranium ore. After multi-parameters screening of aerial radiometric data, patterns recognizing and multi-geoscience information integration analysis, four prospective metallogenetic zones were predicted, and the predication was proved by further geologic survey. Three of the four zones are very encouraging, where ore-forming structures, hydrothermal deposits, wall-rock alteration, primary and secondary uranium ore and rich uranium mineralization are discovered. The department of geologic exploring has decided that these zones will enjoy priority in the examination for further prospecting of uranium ores

Passive Aerial Grasping of Ferrous Objects

KAUST Repository

Fiaz, Usman Amin

2017-10-19

Aerial transportation is probably the most efficient way to supply quick and effective aid especially in cases of emergency like search and rescue operations. Thus the ability to grasp and deliver objects is of vital importance in all sorts of unmanned and autonomous aerial operations. We detail a simple yet novel approach for aerial grasping of ferrous objects using a passive magnetic pickup and an impulse based drop mechanism. The design enables our gripper to grasp ferrous objects using single as well as multiple gripping pads, with visual as well as pickup and drop feedback. We describe the various components of the gripper with emphasis on its low mass and high lift capability since weight is a matter of high consideration in all aerial applications. In addition, we investigate and address the issues that may cause our design to fail. We demonstrate by experiments that the proposed design is robust and effective, based on its high payload capability, its sturdiness against possible slide during aggressive aerial maneuvers, and optimum performance of the drop mechanism for the designed range of payloads. We also show that the gripper is able to pick up and drop a single as well as multiple ferrous objects of different shapes, curvature, and inclination, which also involves picking up an object and then grasping the next, while keeping hold of the previous one.

Passive Aerial Grasping of Ferrous Objects

KAUST Repository

Fiaz, Usman; Toumi, Noureddine; Shamma, Jeff S.

2017-01-01

Aerial transportation is probably the most efficient way to supply quick and effective aid especially in cases of emergency like search and rescue operations. Thus the ability to grasp and deliver objects is of vital importance in all sorts of unmanned and autonomous aerial operations. We detail a simple yet novel approach for aerial grasping of ferrous objects using a passive magnetic pickup and an impulse based drop mechanism. The design enables our gripper to grasp ferrous objects using single as well as multiple gripping pads, with visual as well as pickup and drop feedback. We describe the various components of the gripper with emphasis on its low mass and high lift capability since weight is a matter of high consideration in all aerial applications. In addition, we investigate and address the issues that may cause our design to fail. We demonstrate by experiments that the proposed design is robust and effective, based on its high payload capability, its sturdiness against possible slide during aggressive aerial maneuvers, and optimum performance of the drop mechanism for the designed range of payloads. We also show that the gripper is able to pick up and drop a single as well as multiple ferrous objects of different shapes, curvature, and inclination, which also involves picking up an object and then grasping the next, while keeping hold of the previous one.

Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images

Directory of Open Access Journals (Sweden)

Feimo Li

2017-05-01

Full Text Available Joint vehicle localization and categorization in high resolution aerial images can provide useful information for applications such as traffic flow structure analysis. To maintain sufficient features to recognize small-scaled vehicles, a regions with convolutional neural network features (R-CNN -like detection structure is employed. In this setting, cascaded localization error can be averted by equally treating the negatives and differently typed positives as a multi-class classification task, but the problem of class-imbalance remains. To address this issue, a cost-effective network extension scheme is proposed. In it, the correlated convolution and connection costs during extension are reduced by feature map selection and bi-partite main-side network construction, which are realized with the assistance of a novel feature map class-importance measurement and a new class-imbalance sensitive main-side loss function. By using an image classification dataset established from a set of traditional real-colored aerial images with 0.13 m ground sampling distance which are taken from the height of 1000 m by an imaging system composed of non-metric cameras, the effectiveness of the proposed network extension is verified by comparing with its similarly shaped strong counter-parts. Experiments show an equivalent or better performance, while requiring the least parameter and memory overheads are required.

Moving object detection in top-view aerial videos improved by image stacking

Science.gov (United States)

Teutsch, Michael; Krüger, Wolfgang; Beyerer, Jürgen

2017-08-01

Image stacking is a well-known method that is used to improve the quality of images in video data. A set of consecutive images is aligned by applying image registration and warping. In the resulting image stack, each pixel has redundant information about its intensity value. This redundant information can be used to suppress image noise, resharpen blurry images, or even enhance the spatial image resolution as done in super-resolution. Small moving objects in the videos usually get blurred or distorted by image stacking and thus need to be handled explicitly. We use image stacking in an innovative way: image registration is applied to small moving objects only, and image warping blurs the stationary background that surrounds the moving objects. Our video data are coming from a small fixed-wing unmanned aerial vehicle (UAV) that acquires top-view gray-value images of urban scenes. Moving objects are mainly cars but also other vehicles such as motorcycles. The resulting images, after applying our proposed image stacking approach, are used to improve baseline algorithms for vehicle detection and segmentation. We improve precision and recall by up to 0.011, which corresponds to a reduction of the number of false positive and false negative detections by more than 3 per second. Furthermore, we show how our proposed image stacking approach can be implemented efficiently.

Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks

Science.gov (United States)

Sun, Xiaofeng; Shen, Shuhan; Lin, Xiangguo; Hu, Zhanyi

2017-10-01

High-resolution remote sensing data classification has been a challenging and promising research topic in the community of remote sensing. In recent years, with the rapid advances of deep learning, remarkable progress has been made in this field, which facilitates a transition from hand-crafted features designing to an automatic end-to-end learning. A deep fully convolutional networks (FCNs) based ensemble learning method is proposed to label the high-resolution aerial images. To fully tap the potentials of FCNs, both the Visual Geometry Group network and a deeper residual network, ResNet, are employed. Furthermore, to enlarge training samples with diversity and gain better generalization, in addition to the commonly used data augmentation methods (e.g., rotation, multiscale, and aspect ratio) in the literature, aerial images from other datasets are also collected for cross-scene learning. Finally, we combine these learned models to form an effective FCN ensemble and refine the results using a fully connected conditional random field graph model. Experiments on the ISPRS 2-D Semantic Labeling Contest dataset show that our proposed end-to-end classification method achieves an overall accuracy of 90.7%, a state-of-the-art in the field.

Archaeological Feature Detection from Archive Aerial Photography with a Sfm-Mvs and Image Enhancement Pipeline

Science.gov (United States)

Peppa, M. V.; Mills, J. P.; Fieber, K. D.; Haynes, I.; Turner, S.; Turner, A.; Douglas, M.; Bryan, P. G.

2018-05-01

Understanding and protecting cultural heritage involves the detection and long-term documentation of archaeological remains alongside the spatio-temporal analysis of their landscape evolution. Archive aerial photography can illuminate traces of ancient features which typically appear with different brightness values from their surrounding environment, but are not always well defined. This research investigates the implementation of the Structure-from-Motion - Multi-View Stereo image matching approach with an image enhancement algorithm to derive three epochs of orthomosaics and digital surface models from visible and near infrared historic aerial photography. The enhancement algorithm uses decorrelation stretching to improve the contrast of the orthomosaics so as archaeological features are better detected. Results include 2D / 3D locations of detected archaeological traces stored into a geodatabase for further archaeological interpretation and correlation with benchmark observations. The study also discusses the merits and difficulties of the process involved. This research is based on a European-wide project, entitled "Cultural Heritage Through Time", and the case study research was carried out as a component of the project in the UK.

Small unmanned aerial vehicles (micro-UAVs, drones) in plant ecology1

Science.gov (United States)

Cruzan, Mitchell B.; Weinstein, Ben G.; Grasty, Monica R.; Kohrn, Brendan F.; Hendrickson, Elizabeth C.; Arredondo, Tina M.; Thompson, Pamela G.

2016-01-01

Premise of the study: Low-elevation surveys with small aerial drones (micro–unmanned aerial vehicles [UAVs]) may be used for a wide variety of applications in plant ecology, including mapping vegetation over small- to medium-sized regions. We provide an overview of methods and procedures for conducting surveys and illustrate some of these applications. Methods: Aerial images were obtained by flying a small drone along transects over the area of interest. Images were used to create a composite image (orthomosaic) and a digital surface model (DSM). Vegetation classification was conducted manually and using an automated routine. Coverage of an individual species was estimated from aerial images. Results: We created a vegetation map for the entire region from the orthomosaic and DSM, and mapped the density of one species. Comparison of our manual and automated habitat classification confirmed that our mapping methods were accurate. A species with high contrast to the background matrix allowed adequate estimate of its coverage. Discussion: The example surveys demonstrate that small aerial drones are capable of gathering large amounts of information on the distribution of vegetation and individual species with minimal impact to sensitive habitats. Low-elevation aerial surveys have potential for a wide range of applications in plant ecology. PMID:27672518

Optimal event handling by multiple unmanned aerial vehicles

NARCIS (Netherlands)

de Roo, Martijn; Frasca, Paolo; Carloni, Raffaella

This paper proposes a control architecture for a fleet of unmanned aerial vehicles that is responsible for handling the events that take place in a given area. The architecture guarantees that each event is handled by the required number of vehicles in the shortest time, while the rest of the fleet

Photogrammetric mapping using unmanned aerial vehicle

Science.gov (United States)

Graça, N.; Mitishita, E.; Gonçalves, J.

2014-11-01

Nowadays Unmanned Aerial Vehicle (UAV) technology has attracted attention for aerial photogrammetric mapping. The low cost and the feasibility to automatic flight along commanded waypoints can be considered as the main advantages of this technology in photogrammetric applications. Using GNSS/INS technologies the images are taken at the planned position of the exposure station and the exterior orientation parameters (position Xo, Yo, Zo and attitude ω, φ, χ) of images can be direct determined. However, common UAVs (off-the-shelf) do not replace the traditional aircraft platform. Overall, the main shortcomings are related to: difficulties to obtain the authorization to perform the flight in urban and rural areas, platform stability, safety flight, stability of the image block configuration, high number of the images and inaccuracies of the direct determination of the exterior orientation parameters of the images. In this paper are shown the obtained results from the project photogrammetric mapping using aerial images from the SIMEPAR UAV system. The PIPER J3 UAV Hydro aircraft was used. It has a micro pilot MP2128g. The system is fully integrated with 3-axis gyros/accelerometers, GPS, pressure altimeter, pressure airspeed sensors. A Sony Cyber-shot DSC-W300 was calibrated and used to get the image block. The flight height was close to 400 m, resulting GSD near to 0.10 m. The state of the art of the used technology, methodologies and the obtained results are shown and discussed. Finally advantages/shortcomings found in the study and main conclusions are presented

Aerial image geolocalization by matching its line structure with route map

Science.gov (United States)

Kunina, I. A.; Terekhin, A. P.; Khanipov, T. M.; Kuznetsova, E. G.; Nikolaev, D. P.

2017-03-01

The classic way of aerial photographs geolocation is to bind their local coordinates to a geographic coordinate system using GPS and IMU data. At the same time the possibility of geolocation in a jammed navigation field is also of interest for practical purposes. In this paper we consider one approach to visual localization relatively to a vector road map without GPS. We suggest a geolocalization algorithm which detects image line segments and looks for a geometrical transformation which provides the best mapping between the obtained segments set and line segments in the road map. We consider IMU and altimeter data still known which allows to work with orthorectified images. The problem is hence reduced to a search for a transformation which contains an arbitrary shift and bounded rotation and scaling relatively to the vector map. These parameters are estimated using RANSAC by matching straight line segments from the image to vector map segments. We also investigate how the proposed algorithm's stability is influenced by segment coordinates (two spatial and one angular).

LOW COST SURVEYING USING AN UNMANNED AERIAL VEHICLE

Directory of Open Access Journals (Sweden)

M. Pérez

2013-08-01

Full Text Available Traditional manned airborne surveys are usually expensive and the resolution of the acquired images is often limited. The main advantage of Unmanned Aerial Vehicle (UAV system acting as a photogrammetric sensor platform over more traditional manned airborne system is the high flexibility that allows image acquisition from unconventional viewpoints, the low cost in comparison with classical aerial photogrammetry and the high resolution images obtained. Nowadays there is a necessity for surveying small areas and in these cases, it is not economical the use of normal large format aerial or metric cameras to acquire aerial photos, therefore, the use of UAV platforms can be very suitable. Also the large availability of digital cameras has strongly enhanced the capabilities of UAVs. The use of digital non metric cameras together with the UAV could be used for multiple applications such as aerial surveys, GIS, wildfire mapping, stability of landslides, crop monitoring, etc. The aim of this work was to develop a low cost and accurate methodology in the production of orthophotos and Digital Elevation Models (DEM. The study was conducted in the province of Almeria, south of Spain. The photogrammetric flight had an altitude of 50 m over ground, covering an area of 5.000 m2 approximately. The UAV used in this work was the md4-200, which is an electronic battery powered quadrocopter UAV developed by Microdrones GmbH, Germany. It had on-board a Pextax Optio A40 digital non metric camera with 12 Megapixels. It features a 3x optical zoom lens with a focal range covering angles of view equivalent to those of 37–111 mm lens in 35 mm format. The quadrocopter can be programmed to follow a route defined by several waypoints and actions and it has the ability for vertical take off and landing. Proper flight geometry during image acquisition is essential in order to minimize the number of photographs, avoid areas without a good coverage and make the overlaps

Persistent Aerial Tracking

KAUST Repository

Mueller, Matthias

2016-04-13

In this thesis, we propose a new aerial video dataset and benchmark for low altitude UAV target tracking, as well as, a photo-realistic UAV simulator that can be coupled with tracking methods. Our benchmark provides the rst evaluation of many state of-the-art and popular trackers on 123 new and fully annotated HD video sequences captured from a low-altitude aerial perspective. Among the compared trackers, we determine which ones are the most suitable for UAV tracking both in terms of tracking accuracy and run-time. We also present a simulator that can be used to evaluate tracking algorithms in real-time scenarios before they are deployed on a UAV "in the field", as well as, generate synthetic but photo-realistic tracking datasets with free ground truth annotations to easily extend existing real-world datasets. Both the benchmark and simulator will be made publicly available to the vision community to further research in the area of object tracking from UAVs. Additionally, we propose a persistent, robust and autonomous object tracking system for unmanned aerial vehicles (UAVs) called Persistent Aerial Tracking (PAT). A computer vision and control strategy is applied to a diverse set of moving objects (e.g. humans, animals, cars, boats, etc.) integrating multiple UAVs with a stabilized RGB camera. A novel strategy is employed to successfully track objects over a long period, by \\'handing over the camera\\' from one UAV to another. We integrate the complete system into an off-the-shelf UAV, and obtain promising results showing the robustness of our solution in real-world aerial scenarios.

Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation.

Science.gov (United States)

Ma, Xu; Cheng, Yongmei; Hao, Shuai

2016-12-10

Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

Control of multiple robots using vision sensors

CERN Document Server

Aranda, Miguel; Sagüés, Carlos

2017-01-01

This monograph introduces novel methods for the control and navigation of mobile robots using multiple-1-d-view models obtained from omni-directional cameras. This approach overcomes field-of-view and robustness limitations, simultaneously enhancing accuracy and simplifying application on real platforms. The authors also address coordinated motion tasks for multiple robots, exploring different system architectures, particularly the use of multiple aerial cameras in driving robot formations on the ground. Again, this has benefits of simplicity, scalability and flexibility. Coverage includes details of: a method for visual robot homing based on a memory of omni-directional images a novel vision-based pose stabilization methodology for non-holonomic ground robots based on sinusoidal-varying control inputs an algorithm to recover a generic motion between two 1-d views and which does not require a third view a novel multi-robot setup where multiple camera-carrying unmanned aerial vehicles are used to observe and c...

Selective interferometric imaging of internal multiples

KAUST Repository

Zuberi, M. A H

2013-01-01

Internal multiples deteriorate the image when the imaging procedure assumes only single scattering, especially if the velocity model does not reproduce such scattering in the Green’s function. If properly imaged, internal multiples (and internally-scattered energy) can enhance the seismic image and illuminate areas otherwise neglected or poorly imaged by conventional single-scattering approaches. Conventionally, in order to image internal multiples, accurate, sharp contrasts in the velocity model are required to construct a Green’s function with all the scattered energy. As an alternative, we develop a three-step procedure, which images the first-order internal scattering using the background Green’s function (from the surface to each image point), constructed from a smooth velocity model: We first back-propagate the recorded surface data using the background Green’s function, then cross-correlate the back-propagated data with the recorded data and finally cross-correlate the result with the original background Green’s function. This procedure images the contribution of the recorded first-order internal multiples and is almost free of the single-scattering recorded energy. This image can be added to the conventional single-scattering image, obtained e.g. from Kirchhoff migration, to enhance the image. Application to synthetic data with reflectors illuminated by multiple scattering only demonstrates the effectiveness of the approach.

Applicability of New Approaches of Sensor Orientation to Micro Aerial Vehicles

Science.gov (United States)

Rehak, M.; Skaloud, J.

2016-06-01

This study highlights the benefits of precise aerial position and attitude control in the context of mapping with Micro Aerial Vehicles (MAVs). Accurate mapping with MAVs is gaining importance in applications such as corridor mapping, road and pipeline inspections or mapping of large areas with homogeneous surface structure, e.g. forests or agricultural fields. There, accurate aerial control plays a major role in successful terrain reconstruction and artifact-free ortophoto generation. The presented experiments focus on new approaches of aerial control. We confirm practically that the relative aerial position and attitude control can improve accuracy in difficult mapping scenarios. Indeed, the relative orientation method represents an attractive alternative in the context of MAVs for two reasons. First, the procedure is somewhat simplified, e.g. the angular misalignment, so called boresight, between the camera and the inertial measurement unit (IMU) does not have to be determined and, second, the effect of possible systematic errors in satellite positioning (e.g. due to multipath and/or incorrect recovery of differential carrier-phase ambiguities) is mitigated. First, we present a typical mapping project over an agricultural field and second, we perform a corridor road mapping. We evaluate the proposed methods in scenarios with and without automated image observations. We investigate a recently proposed concept where adjustment is performed using image observations limited to ground control and check points, so called fast aerial triangulation (Fast AT). In this context we show that accurate aerial control (absolute or relative) together with a few image observations can deliver accurate results comparable to classical aerial triangulation with thousands of image measurements. This procedure in turns reduces the demands on processing time and the requirements on the existence of surface texture. Finally, we compare the above mentioned procedures with direct sensor

Persistent Aerial Tracking system for UAVs

KAUST Repository

Mueller, Matthias; Sharma, Gopal; Smith, Neil; Ghanem, Bernard

2016-01-01

In this paper, we propose a persistent, robust and autonomous object tracking system for unmanned aerial vehicles (UAVs) called Persistent Aerial Tracking (PAT). A computer vision and control strategy is applied to a diverse set of moving objects (e.g. humans, animals, cars, boats, etc.) integrating multiple UAVs with a stabilized RGB camera. A novel strategy is employed to successfully track objects over a long period, by ‘handing over the camera’ from one UAV to another. We evaluate several state-of-the-art trackers on the VIVID aerial video dataset and additional sequences that are specifically tailored to low altitude UAV target tracking. Based on the evaluation, we select the leading tracker and improve upon it by optimizing for both speed and performance, integrate the complete system into an off-the-shelf UAV, and obtain promising results showing the robustness of our solution in real-world aerial scenarios.

Persistent Aerial Tracking system for UAVs

KAUST Repository

Mueller, Matthias

2016-12-19

In this paper, we propose a persistent, robust and autonomous object tracking system for unmanned aerial vehicles (UAVs) called Persistent Aerial Tracking (PAT). A computer vision and control strategy is applied to a diverse set of moving objects (e.g. humans, animals, cars, boats, etc.) integrating multiple UAVs with a stabilized RGB camera. A novel strategy is employed to successfully track objects over a long period, by ‘handing over the camera’ from one UAV to another. We evaluate several state-of-the-art trackers on the VIVID aerial video dataset and additional sequences that are specifically tailored to low altitude UAV target tracking. Based on the evaluation, we select the leading tracker and improve upon it by optimizing for both speed and performance, integrate the complete system into an off-the-shelf UAV, and obtain promising results showing the robustness of our solution in real-world aerial scenarios.

Near Real-Time Georeference of Umanned Aerial Vehicle Images for Post-Earthquake Response

Science.gov (United States)

Wang, S.; Wang, X.; Dou, A.; Yuan, X.; Ding, L.; Ding, X.

2018-04-01

The rapid collection of Unmanned Aerial Vehicle (UAV) remote sensing images plays an important role in the fast submitting disaster information and the monitored serious damaged objects after the earthquake. However, for hundreds of UAV images collected in one flight sortie, the traditional data processing methods are image stitching and three-dimensional reconstruction, which take one to several hours, and affect the speed of disaster response. If the manual searching method is employed, we will spend much more time to select the images and the find images do not have spatial reference. Therefore, a near-real-time rapid georeference method for UAV remote sensing disaster data is proposed in this paper. The UAV images are achieved georeference combined with the position and attitude data collected by UAV flight control system, and the georeferenced data is organized by means of world file which is developed by ESRI. The C # language is adopted to compile the UAV images rapid georeference software, combined with Geospatial Data Abstraction Library (GDAL). The result shows that it can realize rapid georeference of remote sensing disaster images for up to one thousand UAV images within one minute, and meets the demand of rapid disaster response, which is of great value in disaster emergency application.

NEAR REAL-TIME GEOREFERENCE OF UMANNED AERIAL VEHICLE IMAGES FOR POST-EARTHQUAKE RESPONSE

Directory of Open Access Journals (Sweden)

S. Wang

2018-04-01

Full Text Available The rapid collection of Unmanned Aerial Vehicle (UAV remote sensing images plays an important role in the fast submitting disaster information and the monitored serious damaged objects after the earthquake. However, for hundreds of UAV images collected in one flight sortie, the traditional data processing methods are image stitching and three-dimensional reconstruction, which take one to several hours, and affect the speed of disaster response. If the manual searching method is employed, we will spend much more time to select the images and the find images do not have spatial reference. Therefore, a near-real-time rapid georeference method for UAV remote sensing disaster data is proposed in this paper. The UAV images are achieved georeference combined with the position and attitude data collected by UAV flight control system, and the georeferenced data is organized by means of world file which is developed by ESRI. The C # language is adopted to compile the UAV images rapid georeference software, combined with Geospatial Data Abstraction Library (GDAL. The result shows that it can realize rapid georeference of remote sensing disaster images for up to one thousand UAV images within one minute, and meets the demand of rapid disaster response, which is of great value in disaster emergency application.

Registration of Aerial Image with Airborne LiDAR Data Based on Plücker Line

Directory of Open Access Journals (Sweden)

SHENG Qinghong

2015-07-01

Full Text Available Registration of aerial image with airborne LiDAR data is a key to feature extraction. A registration model based on Plücker line is proposed. The relative position and attitude relationship between the conjugate lines in LiDAR and image is determined based on Plücker linear equation, which describes line transformation in space, then coplanarity condition equation is established. Finally, coordinate transformation between image point and corresponding LiDAR point is achieved by the spiral movement of Plücker lines in the image. The registration model of Plücker linear coplanarity condition equation is simple, and jointly describes the rotation and translation to avoid coupling error between them, so the accuracy is approved. This research provides technical support for high-quality earth spatial information acquisition.

Robust vehicle detection in aerial images based on salient region selection and superpixel classification

Science.gov (United States)

Sahli, Samir; Duval, Pierre-Luc; Sheng, Yunlong; Lavigne, Daniel A.

2011-05-01

For detecting vehicles in large scale aerial images we first used a non-parametric method proposed recently by Rosin to define the regions of interest, where the vehicles appear with dense edges. The saliency map is a sum of distance transforms (DT) of a set of edges maps, which are obtained by a threshold decomposition of the gradient image with a set of thresholds. A binary mask for highlighting the regions of interest is then obtained by a moment-preserving thresholding of the normalized saliency map. Secondly, the regions of interest were over-segmented by the SLIC superpixels proposed recently by Achanta et al. to cluster pixels into the color constancy sub-regions. In the aerial images of 11.2 cm/pixel resolution, the vehicles in general do not exceed 20 x 40 pixels. We introduced a size constraint to guarantee no superpixels exceed the size of a vehicle. The superpixels were then classified to vehicle or non-vehicle by the Support Vector Machine (SVM), in which the Scale Invariant Feature Transform (SIFT) features and the Linear Binary Pattern (LBP) texture features were used. Both features were extracted at two scales with two size patches. The small patches capture local structures and the larger patches include the neighborhood information. Preliminary results show a significant gain in the detection. The vehicles were detected with a dense concentration of the vehicle-class superpixels. Even dark color cars were successfully detected. A validation process will follow to reduce the presence of isolated false alarms in the background.

Semantic Segmentation of Aerial Images with AN Ensemble of Cnns

Science.gov (United States)

Marmanis, D.; Wegner, J. D.; Galliani, S.; Schindler, K.; Datcu, M.; Stilla, U.

2016-06-01

This paper describes a deep learning approach to semantic segmentation of very high resolution (aerial) images. Deep neural architectures hold the promise of end-to-end learning from raw images, making heuristic feature design obsolete. Over the last decade this idea has seen a revival, and in recent years deep convolutional neural networks (CNNs) have emerged as the method of choice for a range of image interpretation tasks like visual recognition and object detection. Still, standard CNNs do not lend themselves to per-pixel semantic segmentation, mainly because one of their fundamental principles is to gradually aggregate information over larger and larger image regions, making it hard to disentangle contributions from different pixels. Very recently two extensions of the CNN framework have made it possible to trace the semantic information back to a precise pixel position: deconvolutional network layers undo the spatial downsampling, and Fully Convolution Networks (FCNs) modify the fully connected classification layers of the network in such a way that the location of individual activations remains explicit. We design a FCN which takes as input intensity and range data and, with the help of aggressive deconvolution and recycling of early network layers, converts them into a pixelwise classification at full resolution. We discuss design choices and intricacies of such a network, and demonstrate that an ensemble of several networks achieves excellent results on challenging data such as the ISPRS semantic labeling benchmark, using only the raw data as input.

Low-resolution ship detection from high-altitude aerial images

Science.gov (United States)

Qi, Shengxiang; Wu, Jianmin; Zhou, Qing; Kang, Minyang

2018-02-01

Ship detection from optical images taken by high-altitude aircrafts such as unmanned long-endurance airships and unmanned aerial vehicles has broad applications in marine fishery management, ship monitoring and vessel salvage. However, the major challenge is the limited capability of information processing on unmanned high-altitude platforms. Furthermore, in order to guarantee the wide detection range, unmanned aircrafts generally cruise at high altitudes, resulting in imagery with low-resolution targets and strong clutters suffered by heavy clouds. In this paper, we propose a low-resolution ship detection method to extract ships from these high-altitude optical images. Inspired by a recent research on visual saliency detection indicating that small salient signals could be well detected by a gradient enhancement operation combined with Gaussian smoothing, we propose the facet kernel filtering to rapidly suppress cluttered backgrounds and delineate candidate target regions from the sea surface. Then, the principal component analysis (PCA) is used to compute the orientation of the target axis, followed by a simplified histogram of oriented gradient (HOG) descriptor to characterize the ship shape property. Finally, support vector machine (SVM) is applied to discriminate real targets and false alarms. Experimental results show that the proposed method actually has high efficiency in low-resolution ship detection.

Aerial image measurement technique for automated reticle defect disposition (ARDD) in wafer fabs

Science.gov (United States)

Zibold, Axel M.; Schmid, Rainer M.; Stegemann, B.; Scheruebl, Thomas; Harnisch, Wolfgang; Kobiyama, Yuji

2004-08-01

The Aerial Image Measurement System (AIMS)* for 193 nm lithography emulation has been brought into operation successfully worldwide. A second generation system comprising 193 nm AIMS capability, mini-environment and SMIF, the AIMS fab 193 plus is currently introduced into the market. By adjustment of numerical aperture (NA), illumination type and partial illumination coherence to match the conditions in 193 nm steppers or scanners, it can emulate the exposure tool for any type of reticles like binary, OPC and PSM down to the 65 nm node. The system allows a rapid prediction of wafer printability of defects or defect repairs, and critical features, like dense patterns or contacts on the masks without the need to perform expensive image qualification consisting of test wafer exposures followed by SEM measurements. Therefore, AIMS is a mask quality verification standard for high-end photo masks and established in mask shops worldwide. The progress on the AIMS technology described in this paper will highlight that besides mask shops there will be a very beneficial use of the AIMS in the wafer fab and we propose an Automated Reticle Defect Disposition (ARDD) process. With smaller nodes, where design rules are 65 nm or less, it is expected that smaller defects on reticles will occur in increasing numbers in the wafer fab. These smaller mask defects will matter more and more and become a serious yield limiting factor. With increasing mask prices and increasing number of defects and severability on reticles it will become cost beneficial to perform defect disposition on the reticles in wafer production. Currently ongoing studies demonstrate AIMS benefits for wafer fab applications. An outlook will be given for extension of 193 nm aerial imaging down to the 45 nm node based on emulation of immersion scanners.

CLASSIFICATION OF CROP-SHELTER COVERAGE BY RGB AERIAL IMAGES: A COMPENDIUM OF EXPERIENCES AND FINDINGS

Directory of Open Access Journals (Sweden)

Claudia Arcidiacono

2010-09-01

Full Text Available Image processing is a powerful tool apt to perform selective data extraction from high-content images. In agricultural studies, image processing has been applied to different scopes, among them the classification of crop shelters has been recently considered especially in areas where there is a lack of public control in the building activity. The application of image processing to crop-shelter feature recognition make it possible to automatically produce thematic maps that constitute a basic knowledge for local authorities to cope with environmental problems and for technicians to be used in their planning activity. This paper reviews the authors’ experience in the definition of methodologies, based on the main image processing methods, for crop-shelter feature extraction from aerial digital images. Some experiences of pixel-based and object-oriented methods are described and discussed. The results show that the methodology based on object-oriented methods improves crop-shelter classification and reduces computational time, compared to pixel-based methodologies.

Superresolution Imaging Using Resonant Multiples

KAUST Repository

Guo, Bowen

2017-12-22

A resonant multiple is defined as a multiple reflection that revisits the same subsurface location along coincident reflection raypaths. We show that resonant first-order multiples can be migrated with either Kirchhoff or wave-equation migration methods to give images with approximately twice the spatial resolution compared to post-stack primary-reflection images. A moveout-correction stacking method is proposed to enhance the signal-to-noise ratios (SNRs) of the resonant multiples before superresolution migration. The effectiveness of this procedure is validated by synthetic and field data tests.

Superresolution Imaging Using Resonant Multiples

KAUST Repository

Guo, Bowen; Schuster, Gerard T.

2017-01-01

A resonant multiple is defined as a multiple reflection that revisits the same subsurface location along coincident reflection raypaths. We show that resonant first-order multiples can be migrated with either Kirchhoff or wave-equation migration methods to give images with approximately twice the spatial resolution compared to post-stack primary-reflection images. A moveout-correction stacking method is proposed to enhance the signal-to-noise ratios (SNRs) of the resonant multiples before superresolution migration. The effectiveness of this procedure is validated by synthetic and field data tests.

Automatic digital surface model (DSM) generation from aerial imagery data

Science.gov (United States)

Zhou, Nan; Cao, Shixiang; He, Hongyan; Xing, Kun; Yue, Chunyu

2018-04-01

Aerial sensors are widely used to acquire imagery for photogrammetric and remote sensing application. In general, the images have large overlapped region, which provide a lot of redundant geometry and radiation information for matching. This paper presents a POS supported dense matching procedure for automatic DSM generation from aerial imagery data. The method uses a coarse-to-fine hierarchical strategy with an effective combination of several image matching algorithms: image radiation pre-processing, image pyramid generation, feature point extraction and grid point generation, multi-image geometrically constraint cross-correlation (MIG3C), global relaxation optimization, multi-image geometrically constrained least squares matching (MIGCLSM), TIN generation and point cloud filtering. The image radiation pre-processing is used in order to reduce the effects of the inherent radiometric problems and optimize the images. The presented approach essentially consists of 3 components: feature point extraction and matching procedure, grid point matching procedure and relational matching procedure. The MIGCLSM method is used to achieve potentially sub-pixel accuracy matches and identify some inaccurate and possibly false matches. The feasibility of the method has been tested on different aerial scale images with different landcover types. The accuracy evaluation is based on the comparison between the automatic extracted DSMs derived from the precise exterior orientation parameters (EOPs) and the POS.

Generalized internal multiple imaging

KAUST Repository

Zuberi, M. A. H.; Alkhalifah, Tariq Ali

2014-01-01

Internal multiples deteriorate the image when the imaging procedure assumes only single scattering, especially if the velocity model does not have sharp contrasts to reproduce such scattering in the Green’s function through forward modeling

Mapping rock forming minerals at Boundary Canyon, Death Valey National Park, California, using aerial SEBASS thermal infrared hyperspectral image data

Science.gov (United States)

Aslett, Zan; Taranik, James V.; Riley, Dean N.

2018-02-01

Aerial spatially enhanced broadband array spectrograph system (SEBASS) long-wave infrared (LWIR) hyperspectral image data were used to map the distribution of rock-forming minerals indicative of sedimentary and meta-sedimentary lithologies around Boundary Canyon, Death Valley, California, USA. Collection of data over the Boundary Canyon detachment fault (BCDF) facilitated measurement of numerous lithologies representing a contact between the relatively unmetamorphosed Grapevine Mountains allochthon and the metamorphosed core complex of the Funeral Mountains autochthon. These included quartz-rich sandstone, quartzite, conglomerate, and alluvium; muscovite-rich schist, siltstone, and slate; and carbonate-rich dolomite, limestone, and marble, ranging in age from late Precambrian to Quaternary. Hyperspectral data were reduced in dimensionality and processed to statistically identify and map unique emissivity spectra endmembers. Some minerals (e.g., quartz and muscovite) dominate multiple lithologies, resulting in a limited ability to differentiate them. Abrupt variations in image data emissivity amongst pelitic schists corresponded to amphibolite; these rocks represent gradation from greenschist- to amphibolite-metamorphic facies lithologies. Although the full potential of LWIR hyperspectral image data may not be fully utilized within this study area due to lack of measurable spectral distinction between rocks of similar bulk mineralogy, the high spectral resolution of the image data was useful in characterizing silicate- and carbonate-based sedimentary and meta-sedimentary rocks in proximity to fault contacts, as well as for interpreting some mineral mixtures.

Online Aerial Terrain Mapping for Ground Robot Navigation

Directory of Open Access Journals (Sweden)

John Peterson

2018-02-01

Full Text Available This work presents a collaborative unmanned aerial and ground vehicle system which utilizes the aerial vehicle’s overhead view to inform the ground vehicle’s path planning in real time. The aerial vehicle acquires imagery which is assembled into a orthomosaic and then classified. These terrain classes are used to estimate relative navigation costs for the ground vehicle so energy-efficient paths may be generated and then executed. The two vehicles are registered in a common coordinate frame using a real-time kinematic global positioning system (RTK GPS and all image processing is performed onboard the unmanned aerial vehicle, which minimizes the data exchanged between the vehicles. This paper describes the architecture of the system and quantifies the registration errors between the vehicles.

Pre-harvest assessment of perennial weeds in cereals based on images from unmanned aerial systems (UAS)

DEFF Research Database (Denmark)

Egilsson, Jon; Pedersen, Kim Steenstrup; Olsen, Søren Ingvor

2015-01-01

Unmanned aerial systems (UAS) are able to deliver images of agricultural fields of high spatial and temporal resolution. It is, however, not trivial to extract quantitative information about weed infestations from images. This study contributes to weed research by using state-of-the-art computer....... In order to provide ground truth prior to the modeling phase in Python, a subset of 600 images was annotated by experts with 16000 regions of weeds or crop. Following this, images were segmented into regions with weeds or crop by subdividing each image into 64 by 64 pixel patches and classifying each patch...... as either crop or weed. A collection of geo-referenced segmented images may subsequently be used to map weed occurrences in fields. To find a robust and fully automated assessment method both texture and color information was used to build a number of different competing weed-crop classifiers, including...

Pareto-depth for multiple-query image retrieval.

Science.gov (United States)

Hsiao, Ko-Jen; Calder, Jeff; Hero, Alfred O

2015-02-01

Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information. In this paper, we consider the content-based image retrieval problem for multiple query images corresponding to different image semantics. We propose a novel multiple-query information retrieval algorithm that combines the Pareto front method with efficient manifold ranking. We show that our proposed algorithm outperforms state of the art multiple-query retrieval algorithms on real-world image databases. We attribute this performance improvement to concavity properties of the Pareto fronts, and prove a theoretical result that characterizes the asymptotic concavity of the fronts.

AERIAL TERRAIN MAPPING USING UNMANNED AERIAL VEHICLE APPROACH

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K. N. Tahar

2012-08-01

Full Text Available This paper looks into the latest achievement in the low-cost Unmanned Aerial Vehicle (UAV technology in their capacity to map the semi-development areas. The objectives of this study are to establish a new methodology or a new algorithm in image registration during interior orientation process and to determine the accuracy of the photogrammetric products by using UAV images. Recently, UAV technology has been used in several applications such as mapping, agriculture and surveillance. The aim of this study is to scrutinize the usage of UAV to map the semi-development areas. The performance of the low cost UAV mapping study was established on a study area with two image processing methods so that the results could be comparable. A non-metric camera was attached at the bottom of UAV and it was used to capture images at both sites after it went through several calibration steps. Calibration processes were carried out to determine focal length, principal distance, radial lens distortion, tangential lens distortion and affinity. A new method in image registration for a non-metric camera is discussed in this paper as a part of new methodology of this study. This method used the UAV Global Positioning System (GPS onboard to register the UAV image for interior orientation process. Check points were established randomly at both sites using rapid static Global Positioning System. Ground control points are used for exterior orientation process, and check point is used for accuracy assessment of photogrammetric product. All acquired images were processed in a photogrammetric software. Two methods of image registration were applied in this study, namely, GPS onboard registration and ground control point registration. Both registrations were processed by using photogrammetric software and the result is discussed. Two results were produced in this study, which are the digital orthophoto and the digital terrain model. These results were analyzed by using the root

A New Approach to Urban Road Extraction Using High-Resolution Aerial Image

Directory of Open Access Journals (Sweden)

Jianhua Wang

2016-07-01

Full Text Available Road information is fundamental not only in the military field but also common daily living. Automatic road extraction from a remote sensing images can provide references for city planning as well as transportation database and map updating. However, owing to the spectral similarity between roads and impervious structures, the current methods solely using spectral characteristics are often ineffective. By contrast, the detailed information discernible from the high-resolution aerial images enables road extraction with spatial texture features. In this study, a knowledge-based method is established and proposed; this method incorporates the spatial texture feature into urban road extraction. The spatial texture feature is initially extracted by the local Moran’s I, and the derived texture is added to the spectral bands of image for image segmentation. Subsequently, features like brightness, standard deviation, rectangularity, aspect ratio, and area are selected to form the hypothesis and verification model based on road knowledge. Finally, roads are extracted by applying the hypothesis and verification model and are post-processed based on the mathematical morphology. The newly proposed method is evaluated by conducting two experiments. Results show that the completeness, correctness, and quality of the results could reach approximately 94%, 90% and 86% respectively, indicating that the proposed method is effective for urban road extraction.

Automated Snow Extent Mapping Based on Orthophoto Images from Unmanned Aerial Vehicles

Science.gov (United States)

Niedzielski, Tomasz; Spallek, Waldemar; Witek-Kasprzak, Matylda

2018-04-01

The paper presents the application of the k-means clustering in the process of automated snow extent mapping using orthophoto images generated using the Structure-from-Motion (SfM) algorithm from oblique aerial photographs taken by unmanned aerial vehicle (UAV). A simple classification approach has been implemented to discriminate between snow-free and snow-covered terrain. The procedure uses the k-means clustering and classifies orthophoto images based on the three-dimensional space of red-green-blue (RGB) or near-infrared-red-green (NIRRG) or near-infrared-green-blue (NIRGB) bands. To test the method, several field experiments have been carried out, both in situations when snow cover was continuous and when it was patchy. The experiments have been conducted using three fixed-wing UAVs (swinglet CAM by senseFly, eBee by senseFly, and Birdie by FlyTech UAV) on 10/04/2015, 23/03/2016, and 16/03/2017 within three test sites in the Izerskie Mountains in southwestern Poland. The resulting snow extent maps, produced automatically using the classification method, have been validated against real snow extents delineated through a visual analysis and interpretation offered by human analysts. For the simplest classification setup, which assumes two classes in the k-means clustering, the extent of snow patches was estimated accurately, with areal underestimation of 4.6% (RGB) and overestimation of 5.5% (NIRGB). For continuous snow cover with sparse discontinuities at places where trees or bushes protruded from snow, the agreement between automatically produced snow extent maps and observations was better, i.e. 1.5% (underestimation with RGB) and 0.7-0.9% (overestimation, either with RGB or with NIRRG). Shadows on snow were found to be mainly responsible for the misclassification.

Aerial vehicles collision avoidance using monocular vision

Science.gov (United States)

Balashov, Oleg; Muraviev, Vadim; Strotov, Valery

2016-10-01

In this paper image-based collision avoidance algorithm that provides detection of nearby aircraft and distance estimation is presented. The approach requires a vision system with a single moving camera and additional information about carrier's speed and orientation from onboard sensors. The main idea is to create a multi-step approach based on a preliminary detection, regions of interest (ROI) selection, contour segmentation, object matching and localization. The proposed algorithm is able to detect small targets but unlike many other approaches is designed to work with large-scale objects as well. To localize aerial vehicle position the system of equations relating object coordinates in space and observed image is solved. The system solution gives the current position and speed of the detected object in space. Using this information distance and time to collision can be estimated. Experimental research on real video sequences and modeled data is performed. Video database contained different types of aerial vehicles: aircrafts, helicopters, and UAVs. The presented algorithm is able to detect aerial vehicles from several kilometers under regular daylight conditions.

Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.

Science.gov (United States)

Peña, José Manuel; Torres-Sánchez, Jorge; de Castro, Ana Isabel; Kelly, Maggi; López-Granados, Francisca

2013-01-01

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r(2)=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.

The Potential of Unmanned Aerial Vehicle for Large Scale Mapping of Coastal Area

International Nuclear Information System (INIS)

Darwin, N; Ahmad, A; Zainon, O

2014-01-01

Many countries in the tropical region are covered with cloud for most of the time, hence, it is difficult to get clear images especially from high resolution satellite imagery. Aerial photogrammetry can be used but most of the time the cloud problem still exists. Today, this problem could be solved using a system known as unmanned aerial vehicle (UAV) where the aerial images can be acquired at low altitude and the system can fly under the cloud. The UAV system could be used in various applications including mapping coastal area. The UAV system is equipped with an autopilot system and automatic method known as autonomous flying that can be utilized for data acquisition. To achieve high resolution imagery, a compact digital camera of high resolution was used to acquire the aerial images at an altitude. In this study, the UAV system was employed to acquire aerial images of a coastal simulation model at low altitude. From the aerial images, photogrammetric image processing was executed to produce photogrammetric outputs such a digital elevation model (DEM), contour line and orthophoto. In this study, ground control point (GCP) and check point (CP) were established using conventional ground surveying method (i.e total station). The GCP is used for exterior orientation in photogrammetric processes and CP for accuracy assessment based on Root Mean Square Error (RMSE). From this study, it was found that the UAV system can be used for large scale mapping of coastal simulation model with accuracy at millimeter level. It is anticipated that the same system could be used for large scale mapping of real coastal area and produces good accuracy. Finally, the UAV system has great potential to be used for various applications that require accurate results or products at limited time and less man power

Structure-from-Motion Using Historical Aerial Images to Analyse Changes in Glacier Surface Elevation

Directory of Open Access Journals (Sweden)

Nico Mölg

2017-10-01

Full Text Available The application of structure-from-motion (SfM to generate digital terrain models (DTMs derived from different image sources has strongly increased, the major reason for this being that processing is substantially easier with SfM than with conventional photogrammetry. To test the functionality in a demanding environment, we applied SfM and conventional photogrammetry to archival aerial images from Zmuttgletscher, a mountain glacier in Switzerland, for nine dates between 1946 and 2005 using the most popular software packages, and compared the results regarding bundle adjustment and final DTM quality. The results suggest that by using SfM it is possible to produce DTMs of similar quality as with conventional photogrammetry. Higher point cloud density and less noise allow a higher ground resolution of the final DTM, and the time effort from the user is 3–6 times smaller, while the controls of the commercial software packages Agisoft PhotoScan (Version 1.2; Agisoft, St. Petersburg, Russia and Pix4Dmapper (Version 3.0; Pix4D, Lausanne, Switzerland are limited in comparison to ERDAS photogrammetry. SfM performs less reliably when few images with little overlap are processed. Even though SfM facilitates the largely automated production of high quality DTMs, the user is not exempt from a thorough quality check, at best with reference data where available. The resulting DTM time series revealed an average change in surface elevation at the glacier tongue of −67.0 ± 5.3 m. The spatial pattern of changes over time reflects the influence of flow dynamics and the melt of clean ice and that under debris cover. With continued technological advances, we expect to see an increasing use of SfM in glaciology for a variety of purposes, also in processing archival aerial imagery.

Detection of rice sheath blight using an unmanned aerial system with high-resolution color and multispectral imaging.

Science.gov (United States)

Zhang, Dongyan; Zhou, Xingen; Zhang, Jian; Lan, Yubin; Xu, Chao; Liang, Dong

2018-01-01

Detection and monitoring are the first essential step for effective management of sheath blight (ShB), a major disease in rice worldwide. Unmanned aerial systems have a high potential of being utilized to improve this detection process since they can reduce the time needed for scouting for the disease at a field scale, and are affordable and user-friendly in operation. In this study, a commercialized quadrotor unmanned aerial vehicle (UAV), equipped with digital and multispectral cameras, was used to capture imagery data of research plots with 67 rice cultivars and elite lines. Collected imagery data were then processed and analyzed to characterize the development of ShB and quantify different levels of the disease in the field. Through color features extraction and color space transformation of images, it was found that the color transformation could qualitatively detect the infected areas of ShB in the field plots. However, it was less effective to detect different levels of the disease. Five vegetation indices were then calculated from the multispectral images, and ground truths of disease severity and GreenSeeker measured NDVI (Normalized Difference Vegetation Index) were collected. The results of relationship analyses indicate that there was a strong correlation between ground-measured NDVIs and image-extracted NDVIs with the R2 of 0.907 and the root mean square error (RMSE) of 0.0854, and a good correlation between image-extracted NDVIs and disease severity with the R2 of 0.627 and the RMSE of 0.0852. Use of image-based NDVIs extracted from multispectral images could quantify different levels of ShB in the field plots with an accuracy of 63%. These results demonstrate that a customer-grade UAV integrated with digital and multispectral cameras can be an effective tool to detect the ShB disease at a field scale.

Kite Aerial Photography (KAP) as a Tool for Field Teaching

Science.gov (United States)

Sander, Lasse

2014-01-01

Kite aerial photography (KAP) is proposed as a creative tool for geography field teaching and as a medium to approach the complexity of readily available geodata. The method can be integrated as field experiment, surveying technique or group activity. The acquired aerial images can instantaneously be integrated in geographic information systems…

Investigation of an Autofocusing Method for Visible Aerial Cameras Based on Image Processing Techniques

Directory of Open Access Journals (Sweden)

Zhichao Chen

2016-01-01

Full Text Available In order to realize the autofocusing in aerial camera, an autofocusing system is established and its characteristics such as working principle and optical-mechanical structure and focus evaluation function are investigated. The reason for defocusing in aviation camera is analyzed and several autofocusing methods along with appropriate focus evaluation functions are introduced based on the image processing techniques. The proposed autofocusing system is designed and implemented using two CMOS detectors. The experiment results showed that the proposed method met the aviation camera focusing accuracy requirement, and a maximum focusing error of less than half of the focus depth is achieved. The system designed in this paper can find the optical imaging focal plane in real-time; as such, this novel design has great potential in practical engineering, especially aerospace applications.

Autonomous aerial vehicles : guidance, control, signal and image processing platform

International Nuclear Information System (INIS)

Al-Jarrah, M.; Adiansyah, S.; Marji, Z. M.; Chowdhury, M. S.

2011-01-01

The use of unmanned systems is gaining momentum in civil applications after successful use by the armed forces around the globe. Autonomous aerial vehicles are important for providing assistance in monitoring highways, power grid lines, borders, and surveillance of critical infrastructures. It is envisioned that cargo shipping will be completely handled by UAVs by the 2025. Civil use of unmanned autonomous systems brings serious challenges. The need for cost effectiveness, reliability, operation simplicity, safety, and cooperation with human and with other agents are among these challenges. Aerial vehicles operating in the civilian aerospace is the ultimate goal which requires these systems to achieve the reliability of manned aircraft while maintaining their cost effectiveness. In this presentation the development of an autonomous fixed and rotary wing aerial vehicle will be discussed. The architecture of the system from the mission requirements to low level auto pilot control laws will be discussed. Trajectory tracking and path following guidance and control algorithms commonly used and their implementation using of the shelf low cost components will be presented. Autonomous takeo? landing is a key feature that was implemented onboard the vehicle to complete its degree of autonomy. This is implemented based on accurate air-data system designed and fused with sonar measurements, INS/GPS measurements, and vector field method guidance laws. The outcomes of the proposed research is that the AUS-UAV platform named MAZARI is capable of autonomous takeoff and landing based on a pre scheduled flight path using way point navigation and sensor fusion of the inertial navigation system (INS) and global positioning system (GPS). Several technologies need to be mastered when developing a UAV. The navigation task and the need to fuse sensory information to estimate the location of the vehicle is critical to successful autonomous vehicle. Currently extended Kalman filtering is

Aerial imaging for FABs: productivity and yield aspects

Science.gov (United States)

Englard, Ilan; Cohen, Yaron; Elblinger, Yair; Attal, Shay; Berns, Neil; Shoval, Lior; Ben-Yishai, Michael; Mangan, Shmoolik

2009-03-01

The economy of wafer fabs is changing faster for 3x geometry requirements and below. Mask set and exposure tool costs are almost certain to increase the overall cost per die requiring manufacturers to develop productivity and yield improvements to defray the lithography cell economic burden. Lithography cell cost effectiveness can be significantly improved by increasing mask availability while reducing the amount of mask sets needed during a product life cycle. Further efficiency can be gained from reducing send-ahead wafers and qualification cycle time, and elimination of inefficient metrology. Yield is the overriding die cost modulator and is significantly more sensitive to lithography as a result of masking steps required to fabricate the integrated circuit. Thus, for productivity to increase with minimal yield risk, the sample space of reticle induced source of variations should be large, with shortest measurement acquisition time possible. This paper presents the latest introduction of mask aerial imaging technology for the fab, Aera2TM for Lithography with IntenCTM, as an enabler for efficient lithography manufacturing. IntenCD is a high throughput, high density mask-based critical dimension (CD) mapping technology, with the potential for increasing productivity and yield in a wafer production environment. Connecting IntenCD to a feed forward advance process control (APC) reduces significantly the amount of traditional CD metrology required for robust wafer CD uniformity (CDU) correction and increases wafer CD uniformity. This in turn improves the lithography process window and yield and contributes to cost reduction and cycle time reduction of new reticles qualification. Advanced mask technology has introduced a new challenge. Exposure to 193nm wavelength stimulates haze growth on the mask and imposes a regular cleaning schedule. Cleaning eventually causes mask degradation. Haze growth impacts mask CD uniformity and induce global transmission fingerprint

Kite aerial photography (KAP) as a tool for field teaching

DEFF Research Database (Denmark)

Sander, Lasse

2014-01-01

Kite aerial photography (KAP) is proposed as a creative tool for geography field teaching and as a medium to approach the complexity of readily available geodata. The method can be integrated as field experiment, surveying technique or group activity. The acquired aerial images can instantaneousl...... a new vantage point to the fieldwork experience....

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification

Directory of Open Access Journals (Sweden)

Yunlong Yu

2018-01-01

Full Text Available One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references.

An Aerial Video Stabilization Method Based on SURF Feature

Directory of Open Access Journals (Sweden)

Wu Hao

2016-01-01

Full Text Available The video captured by Micro Aerial Vehicle is often degraded due to unexpected random trembling and jitter caused by wind and the shake of the aerial platform. An approach for stabilizing the aerial video based on SURF feature and Kalman filter is proposed. SURF feature points are extracted in each frame, and the feature points between adjacent frames are matched using Fast Library for Approximate Nearest Neighbors search method. Then Random Sampling Consensus matching algorithm and Least Squares Method are used to remove mismatching points pairs, and estimate the transformation between the adjacent images. Finally, Kalman filter is applied to smooth the motion parameters and separate Intentional Motion from Unwanted Motion to stabilize the aerial video. Experiments results show that the approach can stabilize aerial video efficiently with high accuracy, and it is robust to the translation, rotation and zooming motion of camera.

AN AERIAL-IMAGE DENSE MATCHING APPROACH BASED ON OPTICAL FLOW FIELD

Directory of Open Access Journals (Sweden)

W. Yuan

2016-06-01

Full Text Available Dense matching plays an important role in many fields, such as DEM (digital evaluation model producing, robot navigation and 3D environment reconstruction. Traditional approaches may meet the demand of accuracy. But the calculation time and out puts density is hardly be accepted. Focus on the matching efficiency and complex terrain surface matching feasibility an aerial image dense matching method based on optical flow field is proposed in this paper. First, some high accurate and uniformed control points are extracted by using the feature based matching method. Then the optical flow is calculated by using these control points, so as to determine the similar region between two images. Second, the optical flow field is interpolated by using the multi-level B-spline interpolation in the similar region and accomplished the pixel by pixel coarse matching. Final, the results related to the coarse matching refinement based on the combined constraint, which recognizes the same points between images. The experimental results have shown that our method can achieve per-pixel dense matching points, the matching accuracy achieves sub-pixel level, and fully meet the three-dimensional reconstruction and automatic generation of DSM-intensive matching’s requirements. The comparison experiments demonstrated that our approach’s matching efficiency is higher than semi-global matching (SGM and Patch-based multi-view stereo matching (PMVS which verifies the feasibility and effectiveness of the algorithm.

D Surface Generation from Aerial Thermal Imagery

Science.gov (United States)

Khodaei, B.; Samadzadegan, F.; Dadras Javan, F.; Hasani, H.

2015-12-01

Aerial thermal imagery has been recently applied to quantitative analysis of several scenes. For the mapping purpose based on aerial thermal imagery, high accuracy photogrammetric process is necessary. However, due to low geometric resolution and low contrast of thermal imaging sensors, there are some challenges in precise 3D measurement of objects. In this paper the potential of thermal video in 3D surface generation is evaluated. In the pre-processing step, thermal camera is geometrically calibrated using a calibration grid based on emissivity differences between the background and the targets. Then, Digital Surface Model (DSM) generation from thermal video imagery is performed in four steps. Initially, frames are extracted from video, then tie points are generated by Scale-Invariant Feature Transform (SIFT) algorithm. Bundle adjustment is then applied and the camera position and orientation parameters are determined. Finally, multi-resolution dense image matching algorithm is used to create 3D point cloud of the scene. Potential of the proposed method is evaluated based on thermal imaging cover an industrial area. The thermal camera has 640×480 Uncooled Focal Plane Array (UFPA) sensor, equipped with a 25 mm lens which mounted in the Unmanned Aerial Vehicle (UAV). The obtained results show the comparable accuracy of 3D model generated based on thermal images with respect to DSM generated from visible images, however thermal based DSM is somehow smoother with lower level of texture. Comparing the generated DSM with the 9 measured GCPs in the area shows the Root Mean Square Error (RMSE) value is smaller than 5 decimetres in both X and Y directions and 1.6 meters for the Z direction.

Multiple Image Radiography With Diffraction Enhanced Imaging For Breast Specimen

International Nuclear Information System (INIS)

Oltulu, Oral; Zhong Zhong; Hasnah, Moumen; Chapman, Dean

2007-01-01

Biological samples are of great interest for many imaging techniques. The samples usually contain small structures and weak absorption properties. The combinations of weak signals with overlying structures make feature recognition difficult in many cases. In the x-ray regime, a relatively new imaging technique Diffraction Enhanced Imaging (DEI) has superior tissue contrast over conventional radiography and is proven to be very sensitive method. Multiple images taken by DEI are called Multiple Image Radiography (MIR). The purpose of this study is to validate the potential application of the method and to show that MIR-DEI method may give more information about the sample

Integrating dynamic and distributed compressive sensing techniques to enhance image quality of the compressive line sensing system for unmanned aerial vehicles application

Science.gov (United States)

Ouyang, Bing; Hou, Weilin; Caimi, Frank M.; Dalgleish, Fraser R.; Vuorenkoski, Anni K.; Gong, Cuiling

2017-07-01

The compressive line sensing imaging system adopts distributed compressive sensing (CS) to acquire data and reconstruct images. Dynamic CS uses Bayesian inference to capture the correlated nature of the adjacent lines. An image reconstruction technique that incorporates dynamic CS in the distributed CS framework was developed to improve the quality of reconstructed images. The effectiveness of the technique was validated using experimental data acquired in an underwater imaging test facility. Results that demonstrate contrast and resolution improvements will be presented. The improved efficiency is desirable for unmanned aerial vehicles conducting long-duration missions.

1:500 Scale Aerial Triangulation Test with Unmanned Airship in Hubei Province

International Nuclear Information System (INIS)

Feifei, Xie; Zongjian, Lin; Dezhu, Gui

2014-01-01

A new UAVS (Unmanned Aerial Vehicle System) for low altitude aerial photogrammetry is introduced for fine surveying and mapping, including the platform airship, sensor system four-combined wide-angle camera and photogrammetry software MAP-AT. It is demonstrated that this low-altitude aerial photogrammetric system meets the precision requirements of 1:500 scale aerial triangulation based on the test of this system in Hubei province, including the working condition of the airship, the quality of image data and the data processing report. This work provides a possibility for fine surveying and mapping

Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response.

Science.gov (United States)

Ofli, Ferda; Meier, Patrick; Imran, Muhammad; Castillo, Carlos; Tuia, Devis; Rey, Nicolas; Briant, Julien; Millet, Pauline; Reinhard, Friedrich; Parkan, Matthew; Joost, Stéphane

2016-03-01

Aerial imagery captured via unmanned aerial vehicles (UAVs) is playing an increasingly important role in disaster response. Unlike satellite imagery, aerial imagery can be captured and processed within hours rather than days. In addition, the spatial resolution of aerial imagery is an order of magnitude higher than the imagery produced by the most sophisticated commercial satellites today. Both the United States Federal Emergency Management Agency (FEMA) and the European Commission's Joint Research Center (JRC) have noted that aerial imagery will inevitably present a big data challenge. The purpose of this article is to get ahead of this future challenge by proposing a hybrid crowdsourcing and real-time machine learning solution to rapidly process large volumes of aerial data for disaster response in a time-sensitive manner. Crowdsourcing can be used to annotate features of interest in aerial images (such as damaged shelters and roads blocked by debris). These human-annotated features can then be used to train a supervised machine learning system to learn to recognize such features in new unseen images. In this article, we describe how this hybrid solution for image analysis can be implemented as a module (i.e., Aerial Clicker) to extend an existing platform called Artificial Intelligence for Disaster Response (AIDR), which has already been deployed to classify microblog messages during disasters using its Text Clicker module and in response to Cyclone Pam, a category 5 cyclone that devastated Vanuatu in March 2015. The hybrid solution we present can be applied to both aerial and satellite imagery and has applications beyond disaster response such as wildlife protection, human rights, and archeological exploration. As a proof of concept, we recently piloted this solution using very high-resolution aerial photographs of a wildlife reserve in Namibia to support rangers with their wildlife conservation efforts (SAVMAP project, http://lasig.epfl.ch/savmap ). The

Attenuation of multiples in image space

Science.gov (United States)

Alvarez, Gabriel F.

In complex subsurface areas, attenuation of 3D specular and diffracted multiples in data space is difficult and inaccurate. In those areas, image space is an attractive alternative. There are several reasons: (1) migration increases the signal-to-noise ratio of the data; (2) primaries are mapped to coherent events in Subsurface Offset Domain Common Image Gathers (SODCIGs) or Angle Domain Common Image Gathers (ADCIGs); (3) image space is regular and smaller; (4) attenuating the multiples in data space leaves holes in the frequency-Wavenumber space that generate artifacts after migration. I develop a new equation for the residual moveout of specular multiples in ADCIGs and use it for the kernel of an apex-shifted Radon transform to focus and separate the primaries from specular and diffracted multiples. Because of small amplitude, phase and kinematic errors in the multiple estimate, we need adaptive matching and subtraction to estimate the primaries. I pose this problem as an iterative least-squares inversion that simultaneously matches the estimates of primaries and multiples to the data. Standard methods match only the estimate of the multiples. I demonstrate with real and synthetic data that the method produces primaries and multiples with little cross-talk. In 3D, the multiples exhibit residual moveout in SODCIGs in in-line and cross-line offsets. They map away from zero subsurface offsets when migrated with the faster velocity of the primaries. In ADCIGs the residual moveout of the primaries as a function of the aperture angle, for a given azimuth, is flat for those angles that illuminate the reflector. The multiples have residual moveout towards increasing depth for increasing aperture angles at all azimuths. As a function of azimuth, the primaries have better azimuth resolution than the multiples at larger aperture angles. I show, with a real 3D dataset, that even below salt, where illumination is poor, the multiples are well attenuated in ADCIGs with the new

Far-field super-resolution imaging of resonant multiples

KAUST Repository

Guo, Bowen

2016-05-20

We demonstrate for the first time that seismic resonant multiples, usually considered as noise, can be used for super-resolution imaging in the far-field region of sources and receivers. Tests with both synthetic data and field data show that resonant multiples can image reflector boundaries with resolutions more than twice the classical resolution limit. Resolution increases with the order of the resonant multiples. This procedure has important applications in earthquake and exploration seismology, radar, sonar, LIDAR (light detection and ranging), and ultrasound imaging, where the multiples can be used to make high-resolution images.

Comparison of Small Unmanned Aerial Vehicles Performance Using Image Processing

Directory of Open Access Journals (Sweden)

Esteban Cano

2017-01-01

Full Text Available Precision agriculture is a farm management technology that involves sensing and then responding to the observed variability in the field. Remote sensing is one of the tools of precision agriculture. The emergence of small unmanned aerial vehicles (sUAV have paved the way to accessible remote sensing tools for farmers. This paper describes the development of an image processing approach to compare two popular off-the-shelf sUAVs: 3DR Iris+ and DJI Phantom 2. Both units are equipped with a camera gimbal attached with a GoPro camera. The comparison of the two sUAV involves a hovering test and a rectilinear motion test. In the hovering test, the sUAV was allowed to hover over a known object and images were taken every quarter of a second for two minutes. For the image processing evaluation, the position of the object in the images was measured and this was used to assess the stability of the sUAV while hovering. In the rectilinear test, the sUAV was allowed to follow a straight path and images of a lined track were acquired. The lines on the images were then measured on how accurate the sUAV followed the path. The hovering test results show that the 3DR Iris+ had a maximum position deviation of 0.64 m (0.126 m root mean square RMS displacement while the DJI Phantom 2 had a maximum deviation of 0.79 m (0.150 m RMS displacement. In the rectilinear motion test, the maximum displacement for the 3DR Iris+ and the DJI phantom 2 were 0.85 m (0.134 m RMS displacement and 0.73 m (0.372 m RMS displacement. These results demonstrated that the two sUAVs performed well in both the hovering test and the rectilinear motion test and thus demonstrated that both sUAVs can be used for civilian applications such as agricultural monitoring. The results also showed that the developed image processing approach can be used to evaluate performance of a sUAV and has the potential to be used as another feedback control parameter for autonomous navigation.

Evaluation of Different Irrigation Methods for an Apple Orchard Using an Aerial Imaging System

Directory of Open Access Journals (Sweden)

Duke M. Bulanon

2016-06-01

Full Text Available Regular monitoring and assessment of crops is one of the keys to optimal crop production. This research presents the development of a monitoring system called the Crop Monitoring and Assessment Platform (C-MAP. The C-MAP is composed of an image acquisition unit which is an off-the-shelf unmanned aerial vehicle (UAV equipped with a multispectral camera (near-infrared, green, blue, and an image processing and analysis component. The experimental apple orchard at the Parma Research and Extension Center of the University of Idaho was used as the target for monitoring and evaluation. Five experimental rows of the orchard were randomly treated with five different irrigation methods. An image processing algorithm to detect individual trees was developed to facilitate the analysis of the rows and it was able to detect over 90% of the trees. The image analysis of the experimental rows was based on vegetation indices and results showed that there was a significant difference in the Enhanced Normalized Difference Vegetation Index (ENDVI among the five different irrigation methods. This demonstrates that the C-MAP has very good potential as a monitoring tool for orchard management.

Free Surface Downgoing VSP Multiple Imaging

Science.gov (United States)

Maula, Fahdi; Dac, Nguyen

2018-03-01

The common usage of a vertical seismic profile is to capture the reflection wavefield (upgoing wavefield) so that it can be used for further well tie or other interpretations. Borehole Seismic (VSP) receivers capture the reflection from below the well trajectory, traditionally no seismic image information above trajectory. The non-traditional way of processing the VSP multiple can be used to expand the imaging above the well trajectory. This paper presents the case study of using VSP downgoing multiples for further non-traditional imaging applications. In general, VSP processing, upgoing and downgoing arrivals are separated during processing. The up-going wavefield is used for subsurface illumination, whereas the downgoing wavefield and multiples are normally excluded from the processing. In a situation where the downgoing wavefield passes the reflectors several times (multiple), the downgoing wavefield carries reflection information. Its benefit is that it can be used for seismic tie up to seabed, and possibility for shallow hazards identifications. One of the concepts of downgoing imaging is widely known as mirror-imaging technique. This paper presents a case study from deep water offshore Vietnam. The case study is presented to demonstrate the robustness of the technique, and the limitations encountered during its processing.

A Robust Photogrammetric Processing Method of Low-Altitude UAV Images

Directory of Open Access Journals (Sweden)

Mingyao Ai

2015-02-01

Full Text Available Low-altitude Unmanned Aerial Vehicles (UAV images which include distortion, illumination variance, and large rotation angles are facing multiple challenges of image orientation and image processing. In this paper, a robust and convenient photogrammetric approach is proposed for processing low-altitude UAV images, involving a strip management method to automatically build a standardized regional aerial triangle (AT network, a parallel inner orientation algorithm, a ground control points (GCPs predicting method, and an improved Scale Invariant Feature Transform (SIFT method to produce large number of evenly distributed reliable tie points for bundle adjustment (BA. A multi-view matching approach is improved to produce Digital Surface Models (DSM and Digital Orthophoto Maps (DOM for 3D visualization. Experimental results show that the proposed approach is robust and feasible for photogrammetric processing of low-altitude UAV images and 3D visualization of products.

The Use of Unmanned Aerial Vehicle for Geothermal Exploitation Monitoring: Khankala Field Example

Directory of Open Access Journals (Sweden)

Sergey V. Cherkasov

2018-06-01

Full Text Available The article is devoted to the use of unmanned aerial vehicle for geothermal waters exploitation monitoring. Development of a geothermal reservoir usually requires a system of wells, pipelines and pumping equipment and control of such a system is quite complicated. In this regard, use of unmanned aerial vehicle is relevant. Two test unmanned aerial vehicle based infrared surveys have been conducted at the Khankala field (Chechen Republic with the Khankala geothermal plant operating at different regimes: during the first survey – with, and the second – without reinjection of used geothermal fluid. Unmanned aerial vehicle Geoscan 201 equipped with digital (Sony DSX-RX1 and thermal imaging (Thermoframe-MX-TTX cameras was used. Besides different images of the geothermal plant obtained by the surveys, 13 thermal anomalies have been identified. Analysis of the shape and temperature facilitated determination of their different sources: fire, heating systems, etc., which was confirmed by a ground reconnaissance. Results of the study demonstrate a high potential of unmanned aerial vehicle based thermal imagery use for environmental and technological monitoring of geothermal fields under operation.

Mapping reflectance anisotropy of a potato canopy using aerial images acquired with an unmanned aerial vehicle

NARCIS (Netherlands)

Roosjen, Peter; Suomalainen, Juha; Bartholomeus, Harm; Kooistra, Lammert; Clevers, Jan

2017-01-01

Viewing and illumination geometry has a strong influence on optical measurements of natural surfaces due to their anisotropic reflectance properties. Typically, cameras on-board unmanned aerial vehicles (UAVs) are affected by this because of their relatively large field of view (FOV) and thus large

Theoretical study for aerial image intensity in resist in high numerical aperture projection optics and experimental verification with one-dimensional patterns

Science.gov (United States)

Shibuya, Masato; Takada, Akira; Nakashima, Toshiharu

2016-04-01

In optical lithography, high-performance exposure tools are indispensable to obtain not only fine patterns but also preciseness in pattern width. Since an accurate theoretical method is necessary to predict these values, some pioneer and valuable studies have been proposed. However, there might be some ambiguity or lack of consensus regarding the treatment of diffraction by object, incoming inclination factor onto image plane in scalar imaging theory, and paradoxical phenomenon of the inclined entrance plane wave onto image in vector imaging theory. We have reconsidered imaging theory in detail and also phenomenologically resolved the paradox. By comparing theoretical aerial image intensity with experimental pattern width for one-dimensional pattern, we have validated our theoretical consideration.

Multiple Segmentation of Image Stacks

DEFF Research Database (Denmark)

Smets, Jonathan; Jaeger, Manfred

2014-01-01

We propose a method for the simultaneous construction of multiple image segmentations by combining a recently proposed “convolution of mixtures of Gaussians” model with a multi-layer hidden Markov random field structure. The resulting method constructs for a single image several, alternative...

Automatic plankton image classification combining multiple view features via multiple kernel learning.

Science.gov (United States)

Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

2017-12-28

Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system

Electromagnetic wave scattering by aerial and ground radar objects

CERN Document Server

Sukharevsky, Oleg I

2014-01-01

Electromagnetic Wave Scattering by Aerial and Ground Radar Objects presents the theory, original calculation methods, and computational results of the scattering characteristics of different aerial and ground radar objects. This must-have book provides essential background for computing electromagnetic wave scattering in the presence of different kinds of irregularities, as well as Summarizes fundamental electromagnetic statements such as the Lorentz reciprocity theorem and the image principleContains integral field representations enabling the study of scattering from various layered structur

Integration of aerial oblique imagery and terrestrial imagery for optimized 3D modeling in urban areas

Science.gov (United States)

Wu, Bo; Xie, Linfu; Hu, Han; Zhu, Qing; Yau, Eric

2018-05-01

Photorealistic three-dimensional (3D) models are fundamental to the spatial data infrastructure of a digital city, and have numerous potential applications in areas such as urban planning, urban management, urban monitoring, and urban environmental studies. Recent developments in aerial oblique photogrammetry based on aircraft or unmanned aerial vehicles (UAVs) offer promising techniques for 3D modeling. However, 3D models generated from aerial oblique imagery in urban areas with densely distributed high-rise buildings may show geometric defects and blurred textures, especially on building façades, due to problems such as occlusion and large camera tilt angles. Meanwhile, mobile mapping systems (MMSs) can capture terrestrial images of close-range objects from a complementary view on the ground at a high level of detail, but do not offer full coverage. The integration of aerial oblique imagery with terrestrial imagery offers promising opportunities to optimize 3D modeling in urban areas. This paper presents a novel method of integrating these two image types through automatic feature matching and combined bundle adjustment between them, and based on the integrated results to optimize the geometry and texture of the 3D models generated from aerial oblique imagery. Experimental analyses were conducted on two datasets of aerial and terrestrial images collected in Dortmund, Germany and in Hong Kong. The results indicate that the proposed approach effectively integrates images from the two platforms and thereby improves 3D modeling in urban areas.

Oblique Aerial Photography Tool for Building Inspection and Damage Assessment

Science.gov (United States)

Murtiyoso, A.; Remondino, F.; Rupnik, E.; Nex, F.; Grussenmeyer, P.

2014-11-01

Aerial photography has a long history of being employed for mapping purposes due to some of its main advantages, including large area imaging from above and minimization of field work. Since few years multi-camera aerial systems are becoming a practical sensor technology across a growing geospatial market, as complementary to the traditional vertical views. Multi-camera aerial systems capture not only the conventional nadir views, but also tilted images at the same time. In this paper, a particular use of such imagery in the field of building inspection as well as disaster assessment is addressed. The main idea is to inspect a building from four cardinal directions by using monoplotting functionalities. The developed application allows to measure building height and distances and to digitize man-made structures, creating 3D surfaces and building models. The realized GUI is capable of identifying a building from several oblique points of views, as well as calculates the approximate height of buildings, ground distances and basic vectorization. The geometric accuracy of the results remains a function of several parameters, namely image resolution, quality of available parameters (DEM, calibration and orientation values), user expertise and measuring capability.

Aerial LED signage by use of crossed-mirror array

Science.gov (United States)

Yamamoto, Hirotsugu; Kujime, Ryousuke; Bando, Hiroki; Suyama, Shiro

2013-03-01

3D representation of digital signage improves its significance and rapid notification of important points. Real 3D display techniques such as volumetric 3D displays are effective for use of 3D for public signs because it provides not only binocular disparity but also motion parallax and other cues, which will give 3D impression even people with abnormal binocular vision. Our goal is to realize aerial 3D LED signs. We have specially designed and fabricated a reflective optical device to form an aerial image of LEDs with a wide field angle. The developed reflective optical device composed of crossed-mirror array (CMA). CMA contains dihedral corner reflectors at each aperture. After double reflection, light rays emitted from an LED will converge into the corresponding image point. The depth between LED lamps is represented in the same depth in the floating 3D image. Floating image of LEDs was formed in wide range of incident angle with a peak reflectance at 35 deg. The image size of focused beam (point spread function) agreed to the apparent aperture size.

Cooperative path planning of unmanned aerial vehicles

CERN Document Server

Tsourdos, Antonios; Shanmugavel, Madhavan

2010-01-01

An invaluable addition to the literature on UAV guidance and cooperative control, Cooperative Path Planning of Unmanned Aerial Vehicles is a dedicated, practical guide to computational path planning for UAVs. One of the key issues facing future development of UAVs is path planning: it is vital that swarm UAVs/ MAVs can cooperate together in a coordinated manner, obeying a pre-planned course but able to react to their environment by communicating and cooperating. An optimized path is necessary in order to ensure a UAV completes its mission efficiently, safely, and successfully. Focussing on the path planning of multiple UAVs for simultaneous arrival on target, Cooperative Path Planning of Unmanned Aerial Vehicles also offers coverage of path planners that are applicable to land, sea, or space-borne vehicles. Cooperative Path Planning of Unmanned Aerial Vehicles is authored by leading researchers from Cranfield University and provides an authoritative resource for researchers, academics and engineers working in...

Hybrid Control of Long-Endurance Aerial Robotic Vehicles for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Deok-Jin Lee

2011-06-01

Full Text Available This paper presents an effective hybrid control approach for building stable wireless sensor networks between heterogeneous unmanned vehicles using long‐ endurance aerial robotic vehicles. For optimal deployment of the aerial vehicles in communication networks, a gradient climbing based self‐estimating control algorithm is utilized to locate the aerial platforms to maintain maximum communication throughputs between distributed multiple nodes. The autonomous aerial robots, which function as communication relay nodes, extract and harvest thermal energy from the atmospheric environment to improve their flight endurance within specified communication coverage areas. The rapidly‐deployable sensor networks with the high‐endurance aerial vehicles can be used for various application areas including environment monitoring, surveillance, tracking, and decision‐making support. Flight test and simulation studies are conducted to evaluate the effectiveness of the proposed hybrid control technique for robust communication networks.

Benefits and limitations of imaging multiples: Mirror migration

KAUST Repository

Hanafy, Sherif M.; Huang, Yunsong; Schuster, Gerard T.

2015-01-01

The benefits and limitations of imaging multiples are reviewed for mirror migration. Synthetic and field data examples are used to characterize the effectiveness of migrating multiples relative to primary imaging.

Benefits and limitations of imaging multiples: Mirror migration

KAUST Repository

Hanafy, Sherif M.

2015-07-01

The benefits and limitations of imaging multiples are reviewed for mirror migration. Synthetic and field data examples are used to characterize the effectiveness of migrating multiples relative to primary imaging.

Structured diagnostic imaging in patients with multiple trauma

International Nuclear Information System (INIS)

Linsenmaier, U.; Rieger, J.; Rock, C.; Pfeifer, K.J.; Reiser, M.; Kanz, K.G.

2002-01-01

Purpose. Development of a concept for structured diagnostic imaging in patients with multiple trauma.Material and methods. Evaluation of data from a prospective trial with over 2400 documented patients with multiple trauma. All diagnostic and therapeutic steps, primary and secondary death and the 90 days lethality were documented.Structured diagnostic imaging of multiple injured patients requires the integration of an experienced radiologist in an interdisciplinary trauma team consisting of anesthesia, radiology and trauma surgery. Radiology itself deserves standardized concepts for equipment, personnel and logistics to perform diagnostic imaging for a 24-h-coverage with constant quality.Results. This paper describes criteria for initiation of a shock room or emergency room treatment, strategies for documentation and interdisciplinary algorithms for the early clinical care coordinating diagnostic imaging and therapeutic procedures following standardized guidelines. Diagnostic imaging consists of basic diagnosis, radiological ABC-rule, radiological follow-up and structured organ diagnosis using CT. Radiological trauma scoring allows improved quality control of diagnosis and therapy of multiple injured patients.Conclusion. Structured diagnostic imaging of multiple injured patients leads to a standardization of diagnosis and therapy and ensures constant process quality. (orig.) [de

Secure image retrieval with multiple keys

Science.gov (United States)

Liang, Haihua; Zhang, Xinpeng; Wei, Qiuhan; Cheng, Hang

2018-03-01

This article proposes a secure image retrieval scheme under a multiuser scenario. In this scheme, the owner first encrypts and uploads images and their corresponding features to the cloud; then, the user submits the encrypted feature of the query image to the cloud; next, the cloud compares the encrypted features and returns encrypted images with similar content to the user. To find the nearest neighbor in the encrypted features, an encryption with multiple keys is proposed, in which the query feature of each user is encrypted by his/her own key. To improve the key security and space utilization, global optimization and Gaussian distribution are, respectively, employed to generate multiple keys. The experiments show that the proposed encryption can provide effective and secure image retrieval for each user and ensure confidentiality of the query feature of each user.

The orthorectified technology for UAV aerial remote sensing image based on the Programmable GPU

International Nuclear Information System (INIS)

Jin, Liu; Ying-cheng, Li; De-long, Li; Chang-sheng, Teng; Wen-hao, Zhang

2014-01-01

Considering the time requirements of the disaster emergency aerial remote sensing data acquisition and processing, this paper introduced the GPU parallel processing in orthorectification algorithm. Meanwhile, our experiments verified the correctness and feasibility of CUDA parallel processing algorithm, and the algorithm can effectively solve the problem of calculation large, time-consuming for ortho rectification process, realized fast processing of UAV airborne remote sensing image orthorectification based on GPU. The experimental results indicate that using the assumption of same accuracy of proposed method with CPU, the processing time is reduced obviously, maximum acceleration can reach more than 12 times, which greatly enhances the emergency surveying and mapping processing of rapid reaction rate, and has a broad application

U. S. Department of Energy Aerial Measuring Systems

Energy Technology Data Exchange (ETDEWEB)

J. J. Lease

1998-10-01

The Aerial Measuring Systems (AMS) is an aerial surveillance system. This system consists of remote sensing equipment to include radiation detectors; multispectral, thermal, radar, and laser scanners; precision cameras; and electronic imaging and still video systems. This equipment, in varying combinations, is mounted in an airplane or helicopter and flown at different heights in specific patterns to gather various types of data. This system is a key element in the US Department of Energy's (DOE) national emergency response assets. The mission of the AMS program is twofold--first, to respond to emergencies involving radioactive materials by conducting aerial surveys to rapidly track and map the contamination that may exist over a large ground area and second, to conduct routinely scheduled, aerial surveys for environmental monitoring and compliance purposes through the use of credible science and technology. The AMS program evolved from an early program, begun by a predecessor to the DOE--the Atomic Energy Commission--to map the radiation that may have existed within and around the terrestrial environments of DOE facilities, which produced, used, or stored radioactive materials.

U. S. Department of Energy Aerial Measuring Systems

International Nuclear Information System (INIS)

Lease, J.J.

1998-01-01

The Aerial Measuring Systems (AMS) is an aerial surveillance system. This system consists of remote sensing equipment to include radiation detectors; multispectral, thermal, radar, and laser scanners; precision cameras; and electronic imaging and still video systems. This equipment, in varying combinations, is mounted in an airplane or helicopter and flown at different heights in specific patterns to gather various types of data. This system is a key element in the US Department of Energy's (DOE) national emergency response assets. The mission of the AMS program is twofold--first, to respond to emergencies involving radioactive materials by conducting aerial surveys to rapidly track and map the contamination that may exist over a large ground area and second, to conduct routinely scheduled, aerial surveys for environmental monitoring and compliance purposes through the use of credible science and technology. The AMS program evolved from an early program, begun by a predecessor to the DOE--the Atomic Energy Commission--to map the radiation that may have existed within and around the terrestrial environments of DOE facilities, which produced, used, or stored radioactive materials

Automatic Feature Detection, Description and Matching from Mobile Laser Scanning Data and Aerial Imagery

Science.gov (United States)

Hussnain, Zille; Oude Elberink, Sander; Vosselman, George

2016-06-01

In mobile laser scanning systems, the platform's position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of nadir aerial and MLSPC ortho images. In the data pre-processing step the MLSPC is patch-wise cropped and converted to ortho images. Furthermore, each aerial image patch covering the area of the corresponding MLSPC patch is also cropped from the aerial image. For feature detection, we implemented an adaptive variant of Harris-operator to automatically detect corner feature points on the vertices of road markings. In feature description phase, we used the LATCH binary descriptor, which is robust to data from different sensors. For descriptor matching, we developed an outlier filtering technique, which exploits the arrangements of relative Euclidean-distances and angles between corresponding sets of feature points. We found that the positioning accuracy of the computed correspondence has achieved the pixel level accuracy, where the image resolution is 12cm. Furthermore, the developed approach is reliable when enough road markings are available in the data sets. We conclude that, in urban areas, the developed approach can reliably extract features necessary to improve the MLSPC accuracy to pixel level.

Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data

Directory of Open Access Journals (Sweden)

Xiangyu Zhuo

2017-04-01

Full Text Available Recent years have witnessed the fast development of UAVs (unmanned aerial vehicles. As an alternative to traditional image acquisition methods, UAVs bridge the gap between terrestrial and airborne photogrammetry and enable flexible acquisition of high resolution images. However, the georeferencing accuracy of UAVs is still limited by the low-performance on-board GNSS and INS. This paper investigates automatic geo-registration of an individual UAV image or UAV image blocks by matching the UAV image(s with a previously taken georeferenced image, such as an individual aerial or satellite image with a height map attached or an aerial orthophoto with a DSM (digital surface model attached. As the biggest challenge for matching UAV and aerial images is in the large differences in scale and rotation, we propose a novel feature matching method for nadir or slightly tilted images. The method is comprised of a dense feature detection scheme, a one-to-many matching strategy and a global geometric verification scheme. The proposed method is able to find thousands of valid matches in cases where SIFT and ASIFT fail. Those matches can be used to geo-register the whole UAV image block towards the reference image data. When the reference images offer high georeferencing accuracy, the UAV images can also be geolocalized in a global coordinate system. A series of experiments involving different scenarios was conducted to validate the proposed method. The results demonstrate that our approach achieves not only decimeter-level registration accuracy, but also comparable global accuracy as the reference images.

Mapping snow depth in complex alpine terrain with close range aerial imagery - estimating the spatial uncertainties of repeat autonomous aerial surveys over an active rock glacier

Science.gov (United States)

Goetz, Jason; Marcer, Marco; Bodin, Xavier; Brenning, Alexander

2017-04-01

Snow depth mapping in open areas using close range aerial imagery is just one of the many cases where developments in structure-from-motion and multi-view-stereo (SfM-MVS) 3D reconstruction techniques have been applied for geosciences - and with good reason. Our ability to increase the spatial resolution and frequency of observations may allow us to improve our understanding of how snow depth distribution varies through space and time. However, to ensure accurate snow depth observations from close range sensing we must adequately characterize the uncertainty related to our measurement techniques. In this study, we explore the spatial uncertainties of snow elevation models for estimation of snow depth in a complex alpine terrain from close range aerial imagery. We accomplish this by conducting repeat autonomous aerial surveys over a snow-covered active-rock glacier located in the French Alps. The imagery obtained from each flight of an unmanned aerial vehicle (UAV) is used to create an individual digital elevation model (DEM) of the snow surface. As result, we obtain multiple DEMs of the snow surface for the same site. These DEMs are obtained from processing the imagery with the photogrammetry software Agisoft Photoscan. The elevation models are also georeferenced within Photoscan using the geotagged imagery from an onboard GNSS in combination with ground targets placed around the rock glacier, which have been surveyed with highly accurate RTK-GNSS equipment. The random error associated with multi-temporal DEMs of the snow surface is estimated from the repeat aerial survey data. The multiple flights are designed to follow the same flight path and altitude above the ground to simulate the optimal conditions of repeat survey of the site, and thus try to estimate the maximum precision associated with our snow-elevation measurement technique. The bias of the DEMs is assessed with RTK-GNSS survey observations of the snow surface elevation of the area on and surrounding

Advanced Tie Feature Matching for the Registration of Mobile Mapping Imaging Data and Aerial Imagery

Science.gov (United States)

Jende, P.; Peter, M.; Gerke, M.; Vosselman, G.

2016-06-01

Mobile Mapping's ability to acquire high-resolution ground data is opposing unreliable localisation capabilities of satellite-based positioning systems in urban areas. Buildings shape canyons impeding a direct line-of-sight to navigation satellites resulting in a deficiency to accurately estimate the mobile platform's position. Consequently, acquired data products' positioning quality is considerably diminished. This issue has been widely addressed in the literature and research projects. However, a consistent compliance of sub-decimetre accuracy as well as a correction of errors in height remain unsolved. We propose a novel approach to enhance Mobile Mapping (MM) image orientation based on the utilisation of highly accurate orientation parameters derived from aerial imagery. In addition to that, the diminished exterior orientation parameters of the MM platform will be utilised as they enable the application of accurate matching techniques needed to derive reliable tie information. This tie information will then be used within an adjustment solution to correct affected MM data. This paper presents an advanced feature matching procedure as a prerequisite to the aforementioned orientation update. MM data is ortho-projected to gain a higher resemblance to aerial nadir data simplifying the images' geometry for matching. By utilising MM exterior orientation parameters, search windows may be used in conjunction with a selective keypoint detection and template matching. Originating from different sensor systems, however, difficulties arise with respect to changes in illumination, radiometry and a different original perspective. To respond to these challenges for feature detection, the procedure relies on detecting keypoints in only one image. Initial tests indicate a considerable improvement in comparison to classic detector/descriptor approaches in this particular matching scenario. This method leads to a significant reduction of outliers due to the limited availability

CADASTRAL AUDIT AND ASSESSMENTS USING UNMANNED AERIAL SYSTEMS

Directory of Open Access Journals (Sweden)

K. Cunningham

2012-09-01

Full Text Available Ground surveys and remote sensing are integral to establishing fair and equitable property valuations necessary for real property taxation. The International Association of Assessing Officers (IAAO has embraced aerial and street-view imaging as part of its standards related to property tax assessments and audits. New technologies, including unmanned aerial systems (UAS paired with imaging sensors, will become more common as local governments work to ensure their cadastre and tax rolls are both accurate and complete. Trends in mapping technology have seen an evolution in platforms from large, expensive manned aircraft to very small, inexpensive UAS. Traditional methods of photogrammetry have also given way to new equipment and sensors: digital cameras, infrared imagers, light detection and ranging (LiDAR laser scanners, and now synthetic aperture radar (SAR. At the University of Alaska Fairbanks (UAF, we work extensively with unmanned aerial systems equipped with each of these newer sensors. UAF has significant experience flying unmanned systems in the US National Airspace, having begun in 1969 with scientific rockets and expanded to unmanned aircraft in 2003. Ongoing field experience allows UAF to partner effectively with outside organizations to test and develop leading-edge research in UAS and remote sensing. This presentation will discuss our research related to various sensors and payloads for mapping. We will also share our experience with UAS and optical systems for creating some of the first cadastral surveys in rural Alaska.

COMPARISON OF POINT CLOUDS DERIVED FROM AERIAL IMAGE MATCHING WITH DATA FROM AIRBORNE LASER SCANNING

Directory of Open Access Journals (Sweden)

Dominik Wojciech

2017-04-01

Full Text Available The aim of this study was to invest igate the properties of point clouds derived from aerial image matching and to compare them with point clouds from airborne laser scanning. A set of aerial images acquired in years 2010 - 2013 over the city of Elblag were used for the analysis. Images were acquired with the use of three digital cameras: DMC II 230, DMC I and DigiCAM60 with a GSD varying from 4.5 cm to 15 cm. Eight sets of images that were used in the study were acquired at different stages of the growing season – from March to December. Two L iDAR point clouds were used for the comparison – one with a density of 1.3 p/m 2 and a second with a density of 10 p/m 2 . Based on the input images point clouds were created with the use of the semi - global matching method. The properties of the obtained poi nt clouds were analyzed in three ways: – b y the comparison of the vertical accuracy of point clouds with reference to a terrain profile surveyed on bare ground with GPS - RTK method – b y visual assessment of point cloud profiles generated both from SGM and LiDAR point clouds – b y visual assessment of a digital surface model generated from a SGM point cloud with reference to a digital surface model generated from a LiDAR point cloud. The conducted studies allowed a number of observations about the quality o f SGM point clouds to be formulated with respect to different factors. The main factors having influence on the quality of SGM point clouds are GSD and base/height ratio. The essential problem related to SGM point clouds are areas covered with vegetation w here SGM point clouds are visibly worse in terms of both accuracy and the representation of terrain surface. It is difficult to expect that in these areas SG M point clouds could replace LiDAR point clouds. This leads to a general conclusion that SGM point clouds are less reliable, more unpredictable and are dependent on more factors than LiDAR point clouds. Nevertheless, SGM point

Unmanned aerial vehicles (UAVs) for surveying marine fauna: a dugong case study.

Science.gov (United States)

Hodgson, Amanda; Kelly, Natalie; Peel, David

2013-01-01

Aerial surveys of marine mammals are routinely conducted to assess and monitor species' habitat use and population status. In Australia, dugongs (Dugong dugon) are regularly surveyed and long-term datasets have formed the basis for defining habitat of high conservation value and risk assessments of human impacts. Unmanned aerial vehicles (UAVs) may facilitate more accurate, human-risk free, and cheaper aerial surveys. We undertook the first Australian UAV survey trial in Shark Bay, western Australia. We conducted seven flights of the ScanEagle UAV, mounted with a digital SLR camera payload. During each flight, ten transects covering a 1.3 km(2) area frequently used by dugongs, were flown at 500, 750 and 1000 ft. Image (photograph) capture was controlled via the Ground Control Station and the capture rate was scheduled to achieve a prescribed 10% overlap between images along transect lines. Images were manually reviewed post hoc for animals and scored according to sun glitter, Beaufort Sea state and turbidity. We captured 6243 images, 627 containing dugongs. We also identified whales, dolphins, turtles and a range of other fauna. Of all possible dugong sightings, 95% (CI = 90%, 98%) were subjectively classed as 'certain' (unmistakably dugongs). Neither our dugong sighting rate, nor our ability to identify dugongs with certainty, were affected by UAV altitude. Turbidity was the only environmental variable significantly affecting the dugong sighting rate. Our results suggest that UAV systems may not be limited by sea state conditions in the same manner as sightings from manned surveys. The overlap between images proved valuable for detecting animals that were masked by sun glitter in the corners of images, and identifying animals initially captured at awkward body angles. This initial trial of a basic camera system has successfully demonstrated that the ScanEagle UAV has great potential as a tool for marine mammal aerial surveys.

Unmanned aerial vehicles (UAVs for surveying marine fauna: a dugong case study.

Directory of Open Access Journals (Sweden)

Amanda Hodgson

Full Text Available Aerial surveys of marine mammals are routinely conducted to assess and monitor species' habitat use and population status. In Australia, dugongs (Dugong dugon are regularly surveyed and long-term datasets have formed the basis for defining habitat of high conservation value and risk assessments of human impacts. Unmanned aerial vehicles (UAVs may facilitate more accurate, human-risk free, and cheaper aerial surveys. We undertook the first Australian UAV survey trial in Shark Bay, western Australia. We conducted seven flights of the ScanEagle UAV, mounted with a digital SLR camera payload. During each flight, ten transects covering a 1.3 km(2 area frequently used by dugongs, were flown at 500, 750 and 1000 ft. Image (photograph capture was controlled via the Ground Control Station and the capture rate was scheduled to achieve a prescribed 10% overlap between images along transect lines. Images were manually reviewed post hoc for animals and scored according to sun glitter, Beaufort Sea state and turbidity. We captured 6243 images, 627 containing dugongs. We also identified whales, dolphins, turtles and a range of other fauna. Of all possible dugong sightings, 95% (CI = 90%, 98% were subjectively classed as 'certain' (unmistakably dugongs. Neither our dugong sighting rate, nor our ability to identify dugongs with certainty, were affected by UAV altitude. Turbidity was the only environmental variable significantly affecting the dugong sighting rate. Our results suggest that UAV systems may not be limited by sea state conditions in the same manner as sightings from manned surveys. The overlap between images proved valuable for detecting animals that were masked by sun glitter in the corners of images, and identifying animals initially captured at awkward body angles. This initial trial of a basic camera system has successfully demonstrated that the ScanEagle UAV has great potential as a tool for marine mammal aerial surveys.

STRUCTURE FROM MOTION (SfM) PROCESSING FOR UNMANNED AERIAL VEHICLE (UAV)

KAUST Repository

Smith, Neil G.; Shalaby, Mohamed; Passone, Luca

2016-01-01

A method of imaging an area using an unmanned aerial vehicle (UAV) collects a plurality of images from a sensor mounted to the UAV. The plurality of images are processed to detect regions that require additional imaging and an updated flight plan and sensor gimbal position plan is created to capture portions of the area identified as requiring additional imaging.

STRUCTURE FROM MOTION (SfM) PROCESSING FOR UNMANNED AERIAL VEHICLE (UAV)

KAUST Repository

Smith, Neil G.

2016-04-07

A method of imaging an area using an unmanned aerial vehicle (UAV) collects a plurality of images from a sensor mounted to the UAV. The plurality of images are processed to detect regions that require additional imaging and an updated flight plan and sensor gimbal position plan is created to capture portions of the area identified as requiring additional imaging.

The linearized inversion of the generalized interferometric multiple imaging

KAUST Repository

Aldawood, Ali

2016-09-06

The generalized interferometric multiple imaging (GIMI) procedure can be used to image duplex waves and other higher order internal multiples. Imaging duplex waves could help illuminate subsurface zones that are not easily illuminated by primaries such as vertical and nearly vertical fault planes, and salt flanks. To image first-order internal multiple, the GIMI framework consists of three datuming steps, followed by applying the zero-lag cross-correlation imaging condition. However, the standard GIMI procedure yields migrated images that suffer from low spatial resolution, migration artifacts, and cross-talk noise. To alleviate these problems, we propose a least-squares GIMI framework in which we formulate the first two steps as a linearized inversion problem when imaging first-order internal multiples. Tests on synthetic datasets demonstrate the ability to localize subsurface scatterers in their true positions, and delineate a vertical fault plane using the proposed method. We, also, demonstrate the robustness of the proposed framework when imaging the scatterers or the vertical fault plane with erroneous migration velocities.

Aerial photogrammetry procedure optimized for micro uav

Directory of Open Access Journals (Sweden)

T. Anai

2014-06-01

Full Text Available This paper proposes the automatic aerial photogrammetry procedure optimized for Micro UAV that has ability of autonomous flight. The most important goal of our proposed method is the reducing the processing cost for fully automatic reconstruction of DSM from a large amount of image obtained from Micro UAV. For this goal, we have developed automatic corresponding point generation procedure using feature point tracking algorithm considering position and attitude information, which obtained from onboard GPS-IMU integrated on Micro UAV. In addition, we have developed the automatic exterior orientation and registration procedure from the automatic generated corresponding points on each image and position and attitude information from Micro UAV. Moreover, in order to reconstruct precise DSM, we have developed the area base matching process which considering edge information. In this paper, we describe processing flow of our automatic aerial photogrammetry. Moreover, the accuracy assessment is also described. Furthermore, some application of automatic reconstruction of DSM will be desired.

The usefulness of low-altitude aerial photography for the assessment of channel morphodynamics of a lowland river

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Ostrowski Piotr

2017-06-01

Full Text Available The paper presents examples of using low-altitude aerial images of a modern river channel, acquired from an ultralight aircraft. The images have been taken for two sections of the Vistula river: in the Małopolska Gorge and near Dęblin and Gołąb. Alongside with research flights, there were also terrestrial investigations, such as echo sounding of the riverbed and geological mapping, carried out in the river channel zone. A comparison of the results of aerial and terrestrial research revealed high clarity of the images, allowing for precise identification of the evidence that indicates the specific course of river channel processes. Aerial images taken from ultralight aircrafts can significantly increase the accuracy of geological surveys of river channel zones in the Polish Lowlands due to low logistic requirements.

Aerial radiation surveys

International Nuclear Information System (INIS)

Jobst, J.

1980-01-01

A recent aerial radiation survey of the surroundings of the Vitro mill in Salt Lake City shows that uranium mill tailings have been removed to many locations outside their original boundary. To date, 52 remote sites have been discovered within a 100 square kilometer aerial survey perimeter surrounding the mill; 9 of these were discovered with the recent aerial survey map. Five additional sites, also discovered by aerial survey, contained uranium ore, milling equipment, or radioactive slag. Because of the success of this survey, plans are being made to extend the aerial survey program to other parts of the Salt Lake valley where diversions of Vitro tailings are also known to exist

Unmanned Aerial Systems and Spectroscopy for Remote Sensing Applications in Archaeology

Science.gov (United States)

Themistocleous, K.; Agapiou, A.; Cuca, B.; Hadjimitsis, D. G.

2015-04-01

Remote sensing has open up new dimensions in archaeological research. Although there has been significant progress in increasing the resolution of space/aerial sensors and image processing, the detection of the crop (and soil marks) formations, which relate to buried archaeological remains, are difficult to detect since these marks may not be visible in the images if observed over different period or at different spatial/spectral resolution. In order to support the improvement of earth observation remote sensing technologies specifically targeting archaeological research, a better understanding of the crop/soil marks formation needs to be studied in detail. In this paper the contribution of both Unmanned Aerial Systems as well ground spectroradiometers is discussed in a variety of examples applied in the eastern Mediterranean region (Cyprus and Greece) as well in Central Europe (Hungary). In- situ spectroradiometric campaigns can be applied for the removal of atmospheric impact to simultaneous satellite overpass images. In addition, as shown in this paper, the systematic collection of ground truth data prior to the satellite/aerial acquisition can be used to detect the optimum temporal and spectral resolution for the detection of stress vegetation related to buried archaeological remains. Moreover, phenological studies of the crops from the area of interest can be simulated to the potential sensors based on their Relative Response Filters and therefore prepare better the satellite-aerial campaigns. Ground data and the use of Unmanned Aerial Systems (UAS) can provide an increased insight for studying the formation of crop and soil marks. New algorithms such as vegetation indices and linear orthogonal equations for the enhancement of crop marks can be developed based on the specific spectral characteristics of the area. As well, UAS can be used for remote sensing applications in order to document, survey and model cultural heritage and archaeological sites.

Seismic reflection imaging, accounting for primary and multiple reflections

Science.gov (United States)

Wapenaar, Kees; van der Neut, Joost; Thorbecke, Jan; Broggini, Filippo; Slob, Evert; Snieder, Roel

2015-04-01

Imaging of seismic reflection data is usually based on the assumption that the seismic response consists of primary reflections only. Multiple reflections, i.e. waves that have reflected more than once, are treated as primaries and are imaged at wrong positions. There are two classes of multiple reflections, which we will call surface-related multiples and internal multiples. Surface-related multiples are those multiples that contain at least one reflection at the earth's surface, whereas internal multiples consist of waves that have reflected only at subsurface interfaces. Surface-related multiples are the strongest, but also relatively easy to deal with because the reflecting boundary (the earth's surface) is known. Internal multiples constitute a much more difficult problem for seismic imaging, because the positions and properties of the reflecting interfaces are not known. We are developing reflection imaging methodology which deals with internal multiples. Starting with the Marchenko equation for 1D inverse scattering problems, we derived 3D Marchenko-type equations, which relate reflection data at the surface to Green's functions between virtual sources anywhere in the subsurface and receivers at the surface. Based on these equations, we derived an iterative scheme by which these Green's functions can be retrieved from the reflection data at the surface. This iterative scheme requires an estimate of the direct wave of the Green's functions in a background medium. Note that this is precisely the same information that is also required by standard reflection imaging schemes. However, unlike in standard imaging, our iterative Marchenko scheme retrieves the multiple reflections of the Green's functions from the reflection data at the surface. For this, no knowledge of the positions and properties of the reflecting interfaces is required. Once the full Green's functions are retrieved, reflection imaging can be carried out by which the primaries and multiples are

Image Alignment for Multiple Camera High Dynamic Range Microscopy

OpenAIRE

Eastwood, Brian S.; Childs, Elisabeth C.

2012-01-01

This paper investigates the problem of image alignment for multiple camera high dynamic range (HDR) imaging. HDR imaging combines information from images taken with different exposure settings. Combining information from multiple cameras requires an alignment process that is robust to the intensity differences in the images. HDR applications that use a limited number of component images require an alignment technique that is robust to large exposure differences. We evaluate the suitability fo...

Multimodality imaging features of hereditary multiple exostoses

OpenAIRE

Kok, H K; Fitzgerald, L; Campbell, N; Lyburn, I D; Munk, P L; Buckley, O; Torreggiani, W C

2013-01-01

Hereditary multiple exostoses (HME) or diaphyseal aclasis is an inherited disorder characterised by the formation of multiple osteochondromas, which are cartilage-capped osseous outgrowths, and the development of associated osseous deformities. Individuals with HME may be asymptomatic or develop clinical symptoms, which prompt imaging studies. Different modalities ranging from plain radiographs to cross-sectional and nuclear medicine imaging studies can be helpful in the diagnosis and detecti...

Multiplicative calculus in biomedical image analysis

NARCIS (Netherlands)

Florack, L.M.J.; Assen, van H.C.

2011-01-01

We advocate the use of an alternative calculus in biomedical image analysis, known as multiplicative (a.k.a. non-Newtonian) calculus. It provides a natural framework in problems in which positive images or positive definite matrix fields and positivity preserving operators are of interest. Indeed,

Magnetic resonance imaging of the spine in multiple myeloma

International Nuclear Information System (INIS)

Tanaka, Masato; Nakahara, Shinnosuke; Koura, Hiroshi; Kai, Nobuo; Asaumi, Koji; Tanaka, Shunsuke; Sezaki, Tatsuo; Fukuda, Shunichi; Sunami, Kazutaka

2000-01-01

The characteristics of diagnostic imaging of the spine in multiple myeloma were examined. Twenty-one patients with stage II-III multiple myeloma (male=12, female=9, mean age=64) underwent MRI of the spine. Other diagnostic imaging modalities used in these patients included, CT bone scintigraphy, and radiography. All images of the spine were assessed and compared with the MRI images. The type of progression was evaluated based on the tumor distribution classification established by Sezaki. T1-weighted images of all 21 patients showed low signals in vertebral bodies, including 14 cases with a focal low signal intensity and 7 cases with diffuse low signal intensity. On the T2-weighted images, 15 of the 21 cases (71%) showed equivalent signals, while T2*-weighted images obtained by the field-echo method yielded high signals in 10 out of 11 cases. It was difficult to differentiate between senile osteoporosis and multiple myeloma by MRI, but CT images clearly distinguished between them. The results suggested that fat-suppressive T1-contrast images and T2*-weighted images are useful in detecting lesions, especially focal low signal intensity lesions. Patients with the multiple-lesion-tumor type of disease were more likely to develop paralysis more than those with the diffuse myeloproliferative type. Thus, the tumor distribution classification established by Sezaki was useful in considering radiotherapy for the treatment of patients at risk of paralysis. Bone scintigraphy revealed accumulation only in spinal lesions caused by compression fractures, while CT appeared to be useful in localizing the diffuse myeloproliferative type of lesions. The problems associated with diagnosis by MRI are differentiation of multiple myeloma from senile osteoporosis and metastatic bone tumors of the spine. There are few specific findings in multiple myeloma. (K.H.)

Assessing a potential solution for spatially referencing of historical aerial photography in South Africa

Science.gov (United States)

Denner, Michele; Raubenheimer, Jacobus H.

2018-05-01

Historical aerial photography has become a valuable commodity in any country, as it provides a precise record of historical land management over time. In a developing country, such as South Africa, that has undergone enormous political and social change over the last years, such photography is invaluable as it provides a clear indication of past injustices and serves as an aid to addressing post-apartheid issues such as land reform and land redistribution. National mapping organisations throughout the world have vast repositories of such historical aerial photography. In order to effectively use these datasets in today's digital environment requires that it be georeferenced to an accuracy that is suitable for the intended purpose. Using image-to-image georeferencing techniques, this research sought to determine the accuracies achievable for ortho-rectifying large volumes of historical aerial imagery, against the national standard for ortho-rectification in South Africa, using two different types of scanning equipment. The research conducted four tests using aerial photography from different time epochs over a period of sixty years, where the ortho-rectification matched each test to an already ortho-rectified mosaic of a developed area of mixed land use. The results of each test were assessed in terms of visual accuracy, spatial accuracy and conformance to the national standard for ortho-rectification in South Africa. The results showed a decrease in the overall accuracy of the image as the epoch range between the historical image and the reference image increased. Recommendations on the applications possible given the different epoch ranges and scanning equipment used are provided.

Toward autonomous avian-inspired grasping for micro aerial vehicles

International Nuclear Information System (INIS)

Thomas, Justin; Loianno, Giuseppe; Polin, Joseph; Kumar, Vijay; Sreenath, Koushil

2014-01-01

Micro aerial vehicles, particularly quadrotors, have been used in a wide range of applications. However, the literature on aerial manipulation and grasping is limited and the work is based on quasi-static models. In this paper, we draw inspiration from agile, fast-moving birds such as raptors, that are able to capture moving prey on the ground or in water, and develop similar capabilities for quadrotors. We address dynamic grasping, an approach to prehensile grasping in which the dynamics of the robot and its gripper are significant and must be explicitly modeled and controlled for successful execution. Dynamic grasping is relevant for fast pick-and-place operations, transportation and delivery of objects, and placing or retrieving sensors. We show how this capability can be realized (a) using a motion capture system and (b) without external sensors relying only on onboard sensors. In both cases we describe the dynamic model, and trajectory planning and control algorithms. In particular, we present a methodology for flying and grasping a cylindrical object using feedback from a monocular camera and an inertial measurement unit onboard the aerial robot. This is accomplished by mapping the dynamics of the quadrotor to a level virtual image plane, which in turn enables dynamically-feasible trajectory planning for image features in the image space, and a vision-based controller with guaranteed convergence properties. We also present experimental results obtained with a quadrotor equipped with an articulated gripper to illustrate both approaches. (papers)

Toward autonomous avian-inspired grasping for micro aerial vehicles.

Science.gov (United States)

Thomas, Justin; Loianno, Giuseppe; Polin, Joseph; Sreenath, Koushil; Kumar, Vijay

2014-06-01

Micro aerial vehicles, particularly quadrotors, have been used in a wide range of applications. However, the literature on aerial manipulation and grasping is limited and the work is based on quasi-static models. In this paper, we draw inspiration from agile, fast-moving birds such as raptors, that are able to capture moving prey on the ground or in water, and develop similar capabilities for quadrotors. We address dynamic grasping, an approach to prehensile grasping in which the dynamics of the robot and its gripper are significant and must be explicitly modeled and controlled for successful execution. Dynamic grasping is relevant for fast pick-and-place operations, transportation and delivery of objects, and placing or retrieving sensors. We show how this capability can be realized (a) using a motion capture system and (b) without external sensors relying only on onboard sensors. In both cases we describe the dynamic model, and trajectory planning and control algorithms. In particular, we present a methodology for flying and grasping a cylindrical object using feedback from a monocular camera and an inertial measurement unit onboard the aerial robot. This is accomplished by mapping the dynamics of the quadrotor to a level virtual image plane, which in turn enables dynamically-feasible trajectory planning for image features in the image space, and a vision-based controller with guaranteed convergence properties. We also present experimental results obtained with a quadrotor equipped with an articulated gripper to illustrate both approaches.

Evaluation of remote sensing aerial systems in existing transportation practices, phase II.

Science.gov (United States)

2011-06-01

A low-cost aerial platform represents a flexible tool for acquiring high-resolution images for ground areas of interest. The geo-referencing of objects within these images could benefit civil engineers in a variety of research areas including, but no...

A typical MR imaging of multiple sclerosis

International Nuclear Information System (INIS)

Katagiri, Shinako; Kan, Shinichi; Ikeda, Toshiaki; Nishiyama, Syougo; Nishimaki, Hiroshi; Matsubayashi, Takashi; Hata, Takashi

1995-01-01

MR imaging is very useful in detecting the intracranial lesion of multiple sclerosis (MS). We present six patients of MS with atypical MR imaging findings. Six patients aged 27-56 years (mean 36 years), and sexuality of six patients were 2 men and 4 females. Three patient's clinical course had episodes of optic neuritis. The plaque's size of the predominant lesion of the patients ranged from 3.0 to 9.0 cm in diameter. The plaques were oval, elliptically and other shaped. At acute stage, MR imaging detected perfocal edema and focal mass effect in three cases of our study. Two out of six cases showed multiple irregularly enhancing lesion with Gadolinium-DTPA. Plaques of all cases did not disappear completely in final MR imaging study. (author)

Automated Registration Of Images From Multiple Sensors

Science.gov (United States)

Rignot, Eric J. M.; Kwok, Ronald; Curlander, John C.; Pang, Shirley S. N.

1994-01-01

Images of terrain scanned in common by multiple Earth-orbiting remote sensors registered automatically with each other and, where possible, on geographic coordinate grid. Simulated image of terrain viewed by sensor computed from ancillary data, viewing geometry, and mathematical model of physics of imaging. In proposed registration algorithm, simulated and actual sensor images matched by area-correlation technique.

Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles

Directory of Open Access Journals (Sweden)

Francisco-Javier Mesas-Carrascosa

2018-04-01

Full Text Available The development of lightweight sensors compatible with mini unmanned aerial vehicles (UAVs has expanded the agronomical applications of remote sensing. Of particular interest in this paper are thermal sensors based on lightweight microbolometer technology. These are mainly used to assess crop water stress with thermal images where an accuracy greater than 1 °C is necessary. However, these sensors lack precise temperature control, resulting in thermal drift during image acquisition that requires correction. Currently, there are several strategies to manage thermal drift effect. However, these strategies reduce useful flight time over crops due to the additional in-flight calibration operations. This study presents a drift correction methodology for microbolometer sensors based on redundant information from multiple overlapping images. An empirical study was performed in an orchard of high-density hedgerow olive trees with flights at different times of the day. Six mathematical drift correction models were developed and assessed to explain and correct drift effect on thermal images. Using the proposed methodology, the resulting thermally corrected orthomosaics yielded a rate of error lower than 1° C compared to those where no drift correction was applied.

Real-Time Multi-Target Localization from Unmanned Aerial Vehicles

Directory of Open Access Journals (Sweden)

Xuan Wang

2016-12-01

Full Text Available In order to improve the reconnaissance efficiency of unmanned aerial vehicle (UAV electro-optical stabilized imaging systems, a real-time multi-target localization scheme based on an UAV electro-optical stabilized imaging system is proposed. First, a target location model is studied. Then, the geodetic coordinates of multi-targets are calculated using the homogeneous coordinate transformation. On the basis of this, two methods which can improve the accuracy of the multi-target localization are proposed: (1 the real-time zoom lens distortion correction method; (2 a recursive least squares (RLS filtering method based on UAV dead reckoning. The multi-target localization error model is established using Monte Carlo theory. In an actual flight, the UAV flight altitude is 1140 m. The multi-target localization results are within the range of allowable error. After we use a lens distortion correction method in a single image, the circular error probability (CEP of the multi-target localization is reduced by 7%, and 50 targets can be located at the same time. The RLS algorithm can adaptively estimate the location data based on multiple images. Compared with multi-target localization based on a single image, CEP of the multi-target localization using RLS is reduced by 25%. The proposed method can be implemented on a small circuit board to operate in real time. This research is expected to significantly benefit small UAVs which need multi-target geo-location functions.

True Orthophoto Generation from Aerial Frame Images and LiDAR Data: An Update

Directory of Open Access Journals (Sweden)

Hamid Gharibi

2018-04-01

Full Text Available Image spectral and Light Detection and Ranging (LiDAR positional information can be related through the orthophoto generation process. Orthophotos have a uniform scale and represent all objects in their correct planimetric locations. However, orthophotos generated using conventional methods suffer from an artifact known as the double-mapping effect that occurs in areas occluded by tall objects. The double-mapping problem can be resolved through the commonly known true orthophoto generation procedure, in which an occlusion detection process is incorporated. This paper presents a review of occlusion detection methods, from which three techniques are compared and analyzed using experimental results. The paper also describes a framework for true orthophoto production based on an angle-based occlusion detection method. To improve the performance of the angle-based technique, two modifications to this method are introduced. These modifications, which aim at resolving false visibilities reported within the angle-based occlusion detection process, are referred to as occlusion extension and radial section overlap. A weighted averaging approach is also proposed to mitigate the seamline effect and spectral dissimilarity that may appear in true orthophoto mosaics. Moreover, true orthophotos generated from high-resolution aerial images and high-density LiDAR data using the updated version of angle-based methodology are illustrated for two urban study areas. To investigate the potential of image matching techniques in producing true orthophotos and point clouds, a comparison between the LiDAR-based and image-matching-based true orthophotos and digital surface models (DSMs for an urban study area is also presented in this paper. Among the investigated occlusion detection methods, the angle-based technique demonstrated a better performance in terms of output and running time. The LiDAR-based true orthophotos and DSMs showed higher qualities compared to their

A typical MR imaging of multiple sclerosis

Energy Technology Data Exchange (ETDEWEB)

Katagiri, Shinako; Kan, Shinichi; Ikeda, Toshiaki; Nishiyama, Syougo; Nishimaki, Hiroshi; Matsubayashi, Takashi; Hata, Takashi [Kitasato Univ., Sagamihara, Kanagawa (Japan). School of Medicine

1995-06-01

MR imaging is very useful in detecting the intracranial lesion of multiple sclerosis (MS). We present six patients of MS with atypical MR imaging findings. Six patients aged 27-56 years (mean 36 years), and sexuality of six patients were 2 men and 4 females. Three patient`s clinical course had episodes of optic neuritis. The plaque`s size of the predominant lesion of the patients ranged from 3.0 to 9.0 cm in diameter. The plaques were oval, elliptically and other shaped. At acute stage, MR imaging detected perfocal edema and focal mass effect in three cases of our study. Two out of six cases showed multiple irregularly enhancing lesion with Gadolinium-DTPA. Plaques of all cases did not disappear completely in final MR imaging study. (author).

A Robust Transform Estimator Based on Residual Analysis and Its Application on UAV Aerial Images

Directory of Open Access Journals (Sweden)

Guorong Cai

2018-02-01

Full Text Available Estimating the transformation between two images from the same scene is a fundamental step for image registration, image stitching and 3D reconstruction. State-of-the-art methods are mainly based on sorted residual for generating hypotheses. This scheme has acquired encouraging results in many remote sensing applications. Unfortunately, mainstream residual based methods may fail in estimating the transform between Unmanned Aerial Vehicle (UAV low altitude remote sensing images, due to the fact that UAV images always have repetitive patterns and severe viewpoint changes, which produce lower inlier rate and higher pseudo outlier rate than other tasks. We performed extensive experiments and found the main reason is that these methods compute feature pair similarity within a fixed window, making them sensitive to the size of residual window. To solve this problem, three schemes that based on the distribution of residuals are proposed, which are called Relational Window (RW, Sliding Window (SW, Reverse Residual Order (RRO, respectively. Specially, RW employs a relaxation residual window size to evaluate the highest similarity within a relaxation model length. SW fixes the number of overlap models while varying the length of window size. RRO takes the permutation of residual values into consideration to measure similarity, not only including the number of overlap structures, but also giving penalty to reverse number within the overlap structures. Experimental results conducted on our own built UAV high resolution remote sensing images show that the proposed three strategies all outperform traditional methods in the presence of severe perspective distortion due to viewpoint change.

Aerial Photography and Imagery, Ortho-Corrected - 2011 Digital Orthophotos - Lee County

Data.gov (United States)

NSGIC Education | GIS Inventory — The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International January 2nd and March 21st, 2011. Automatic aerial...

Preliminary analysis of the forest health state based on multispectral images acquired by Unmanned Aerial Vehicle

Directory of Open Access Journals (Sweden)

Czapski Paweł

2015-09-01

Full Text Available The main purpose of this publication is to present the current progress of the work associated with the use of a lightweight unmanned platforms for various environmental studies. Current development in information technology, electronics and sensors miniaturisation allows mounting multispectral cameras and scanners on unmanned aerial vehicle (UAV that could only be used on board aircraft and satellites. Remote Sensing Division in the Institute of Aviation carries out innovative researches using multisensory platform and lightweight unmanned vehicle to evaluate the health state of forests in Wielkopolska province. In this paper, applicability of multispectral images analysis acquired several times during the growing season from low altitude (up to 800m is presented. We present remote sensing indicators computed by our software and common methods for assessing state of trees health. The correctness of applied methods is verified using analysis of satellite scenes acquired by Landsat 8 OLI instrument (Operational Land Imager.

Using Unmanned Aerial Vehicles (UAVs) to Modeling Tornado Impacts

Science.gov (United States)

Wagner, M.; Doe, R. K.

2017-12-01

Using Unmanned Aerial Vehicles (UAVs) to assess storm damage is a useful research tool. Benefits include their ability to access remote or impassable areas post-storm, identify unknown damages and assist with more detailed site investigations and rescue efforts. Technological advancement of UAVs mean that they can capture high resolution images often at an affordable price. These images can be used to create 3D environments to better interpret and delineate damages from large areas that would have been difficult in ground surveys. This research presents the results of a rapid response site investigation of the 29 April 2017 Canton, Texas, USA, tornado using low cost UAVs. This was a multiple, high impact tornado event measuring EF4 at maximum. Rural farmland was chosen as a challenging location to test both equipment and methodology. Such locations provide multiple impacts at a variety of scales including structural and vegetation damage and even animal fatalities. The 3D impact models allow for a more comprehensive study prior to clean-up. The results show previously unseen damages and better quantify damage impacts at the local level. 3D digital track swaths were created allowing for a more accurate track width determination. These results demonstrate how effective the use of low cost UAVs can be for rapid response storm damage assessments, the high quality of data they can achieve, and how they can help us better visualize tornado site investigations.

A CNN-Based Method of Vehicle Detection from Aerial Images Using Hard Example Mining

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Yohei Koga

2018-01-01

Full Text Available Recently, deep learning techniques have had a practical role in vehicle detection. While much effort has been spent on applying deep learning to vehicle detection, the effective use of training data has not been thoroughly studied, although it has great potential for improving training results, especially in cases where the training data are sparse. In this paper, we proposed using hard example mining (HEM in the training process of a convolutional neural network (CNN for vehicle detection in aerial images. We applied HEM to stochastic gradient descent (SGD to choose the most informative training data by calculating the loss values in each batch and employing the examples with the largest losses. We picked 100 out of both 500 and 1000 examples for training in one iteration, and we tested different ratios of positive to negative examples in the training data to evaluate how the balance of positive and negative examples would affect the performance. In any case, our method always outperformed the plain SGD. The experimental results for images from New York showed improved performance over a CNN trained in plain SGD where the F1 score of our method was 0.02 higher.

Toward Automatic Georeferencing of Archival Aerial Photogrammetric Surveys

Science.gov (United States)

Giordano, S.; Le Bris, A.; Mallet, C.

2018-05-01

Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes over the past 100 years. They provide a relatively dense temporal sampling of the territories with very high spatial resolution. Such time series image analysis is a mandatory baseline for a large variety of long-term environmental monitoring studies. The current bottleneck for accurate comparison between epochs is their fine georeferencing step. No fully automatic method has been proposed yet and existing studies are rather limited in terms of area and number of dates. State-of-the art shows that the major challenge is the identification of ground references: cartographic coordinates and their position in the archival images. This task is manually performed, and extremely time-consuming. This paper proposes to use a photogrammetric approach, and states that the 3D information that can be computed is the key to full automation. Its original idea lies in a 2-step approach: (i) the computation of a coarse absolute image orientation; (ii) the use of the coarse Digital Surface Model (DSM) information for automatic absolute image orientation. It only relies on a recent orthoimage+DSM, used as master reference for all epochs. The coarse orthoimage, compared with such a reference, allows the identification of dense ground references and the coarse DSM provides their position in the archival images. Results on two areas and 5 dates show that this method is compatible with long and dense archival aerial image series. Satisfactory planimetric and altimetric accuracies are reported, with variations depending on the ground sampling distance of the images and the location of the Ground Control Points.

TOWARD AUTOMATIC GEOREFERENCING OF ARCHIVAL AERIAL PHOTOGRAMMETRIC SURVEYS

Directory of Open Access Journals (Sweden)

S. Giordano

2018-05-01

Full Text Available Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes over the past 100 years. They provide a relatively dense temporal sampling of the territories with very high spatial resolution. Such time series image analysis is a mandatory baseline for a large variety of long-term environmental monitoring studies. The current bottleneck for accurate comparison between epochs is their fine georeferencing step. No fully automatic method has been proposed yet and existing studies are rather limited in terms of area and number of dates. State-of-the art shows that the major challenge is the identification of ground references: cartographic coordinates and their position in the archival images. This task is manually performed, and extremely time-consuming. This paper proposes to use a photogrammetric approach, and states that the 3D information that can be computed is the key to full automation. Its original idea lies in a 2-step approach: (i the computation of a coarse absolute image orientation; (ii the use of the coarse Digital Surface Model (DSM information for automatic absolute image orientation. It only relies on a recent orthoimage+DSM, used as master reference for all epochs. The coarse orthoimage, compared with such a reference, allows the identification of dense ground references and the coarse DSM provides their position in the archival images. Results on two areas and 5 dates show that this method is compatible with long and dense archival aerial image series. Satisfactory planimetric and altimetric accuracies are reported, with variations depending on the ground sampling distance of the images and the location of the Ground Control Points.

Ultramap v3 - a Revolution in Aerial Photogrammetry

Science.gov (United States)

Reitinger, B.; Sormann, M.; Zebedin, L.; Schachinger, B.; Hoefler, M.; Tomasi, R.; Lamperter, M.; Gruber, B.; Schiester, G.; Kobald, M.; Unger, M.; Klaus, A.; Bernoegger, S.; Karner, K.; Wiechert, A.; Ponticelli, M.; Gruber, M.

2012-07-01

In the last years, Microsoft has driven innovation in the aerial photogrammetry community. Besides the market leading camera technology, UltraMap has grown to an outstanding photogrammetric workflow system which enables users to effectively work with large digital aerial image blocks in a highly automated way. Best example is the project-based color balancing approach which automatically balances images to a homogeneous block. UltraMap V3 continues innovation, and offers a revolution in terms of ortho processing. A fully automated dense matching module strives for high precision digital surface models (DSMs) which are calculated either on CPUs or on GPUs using a distributed processing framework. By applying constrained filtering algorithms, a digital terrain model can be derived which in turn can be used for fully automated traditional ortho texturing. By having the knowledge about the underlying geometry, seamlines can be generated automatically by applying cost functions in order to minimize visual disturbing artifacts. By exploiting the generated DSM information, a DSMOrtho is created using the balanced input images. Again, seamlines are detected automatically resulting in an automatically balanced ortho mosaic. Interactive block-based radiometric adjustments lead to a high quality ortho product based on UltraCam imagery. UltraMap v3 is the first fully integrated and interactive solution for supporting UltraCam images at best in order to deliver DSM and ortho imagery.

Multiple image x-radiography for functional lung imaging

Science.gov (United States)

Aulakh, G. K.; Mann, A.; Belev, G.; Wiebe, S.; Kuebler, W. M.; Singh, B.; Chapman, D.

2018-01-01

Detection and visualization of lung tissue structures is impaired by predominance of air. However, by using synchrotron x-rays, refraction of x-rays at the interface of tissue and air can be utilized to generate contrast which may in turn enable quantification of lung optical properties. We utilized multiple image radiography, a variant of diffraction enhanced imaging, at the Canadian light source to quantify changes in unique x-ray optical properties of lungs, namely attenuation, refraction and ultra small-angle scatter (USAXS or width) contrast ratios as a function of lung orientation in free-breathing or respiratory-gated mice before and after intra-nasal bacterial endotoxin (lipopolysaccharide) instillation. The lung ultra small-angle scatter and attenuation contrast ratios were significantly higher 9 h post lipopolysaccharide instillation compared to saline treatment whereas the refraction contrast decreased in magnitude. In ventilated mice, end-expiratory pressures result in an increase in ultra small-angle scatter contrast ratio when compared to end-inspiratory pressures. There were no detectable changes in lung attenuation or refraction contrast ratio with change in lung pressure alone. In effect, multiple image radiography can be applied towards following optical properties of lung air-tissue barrier over time during pathologies such as acute lung injury.

Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining.

Science.gov (United States)

Tang, Tianyu; Zhou, Shilin; Deng, Zhipeng; Zou, Huanxin; Lei, Lin

2017-02-10

Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.

Magnetic resonance imaging in clinically-definite multiple sclerosis

International Nuclear Information System (INIS)

Noakes, J.B.; Herkes, G.K.; Frith, J.A.

1990-01-01

Forty-two patients with clinically-definite multiple sclerosis were examined by magnetic resonance imaging using a 1.5-T instrument. Magnetic resonance imaging detected an abnormality in 90% of patients. In four patients, no lesions were demonstrated. The number, size and site of the lesions by magnetic resonance imaging were compared with the patients' clinical status and other variables. The Kurtzke disability status scale score increased in patients with corpus callosum atrophy, brainstem and basal ganglia lesions, and correlated with the total number of lesions. No correlation was shown between the findings of magnetic resonance imaging and disease duration, age, sex or pattern-reversal visual-evoked potentials. The variety of magnetic resonance images that could be obtained in patients with clinically-definite multiple sclerosis is highlighted. 24 refs., 8 figs., 1 tab

Image Alignment for Multiple Camera High Dynamic Range Microscopy.

Science.gov (United States)

Eastwood, Brian S; Childs, Elisabeth C

2012-01-09

This paper investigates the problem of image alignment for multiple camera high dynamic range (HDR) imaging. HDR imaging combines information from images taken with different exposure settings. Combining information from multiple cameras requires an alignment process that is robust to the intensity differences in the images. HDR applications that use a limited number of component images require an alignment technique that is robust to large exposure differences. We evaluate the suitability for HDR alignment of three exposure-robust techniques. We conclude that image alignment based on matching feature descriptors extracted from radiant power images from calibrated cameras yields the most accurate and robust solution. We demonstrate the use of this alignment technique in a high dynamic range video microscope that enables live specimen imaging with a greater level of detail than can be captured with a single camera.

Persistent Aerial Tracking

KAUST Repository

Mueller, Matthias

2016-01-01

persistent, robust and autonomous object tracking system for unmanned aerial vehicles (UAVs) called Persistent Aerial Tracking (PAT). A computer vision and control strategy is applied to a diverse set of moving objects (e.g. humans, animals, cars, boats, etc

Monocular Vision System for Fixed Altitude Flight of Unmanned Aerial Vehicles

Directory of Open Access Journals (Sweden)

Kuo-Lung Huang

2015-07-01

Full Text Available The fastest and most economical method of acquiring terrain images is aerial photography. The use of unmanned aerial vehicles (UAVs has been investigated for this task. However, UAVs present a range of challenges such as flight altitude maintenance. This paper reports a method that combines skyline detection with a stereo vision algorithm to enable the flight altitude of UAVs to be maintained. A monocular camera is mounted on the downside of the aircraft’s nose to collect continuous ground images, and the relative altitude is obtained via a stereo vision algorithm from the velocity of the UAV. Image detection is used to obtain terrain images, and to measure the relative altitude from the ground to the UAV. The UAV flight system can be set to fly at a fixed and relatively low altitude to obtain the same resolution of ground images. A forward-looking camera is mounted on the upside of the aircraft’s nose. In combination with the skyline detection algorithm, this helps the aircraft to maintain a stable flight pattern. Experimental results show that the proposed system enables UAVs to obtain terrain images at constant resolution, and to detect the relative altitude along the flight path.

Method of transmission of dynamic multibit digital images from micro-unmanned aerial vehicles

Science.gov (United States)

Petrov, E. P.; Kharina, N. L.

2018-01-01

In connection with successful usage of nanotechnologies in remote sensing great attention is paid to the systems in micro-unmanned aerial vehicles (MUAVs) capable to provide high spatial resolution of dynamic multibit digital images (MDI). Limited energy resources on board the MUAV do not allow transferring a large amount of video information in the shortest possible time. It keeps back the broad development of MUAV. The search for methods to shorten the transmission time of dynamic MDIs from MUAV over the radio channel leads to the methods of MDI compression without computational operations onboard the MUAV. The known compression codecs of video information can not be applied because of the limited energy resources. In this paper we propose a method for reducing the transmission time of dynamic MDIs without computational operations and distortions onboard the MUAV. To develop the method a mathematical apparatus of the theory of conditional Markov processes with discrete arguments was used. On its basis a mathematical model for the transformation of the MDI represented by binary images (BI) in the MDI, consisting of groups of neighboring BIs (GBI) transmitted by multiphase (MP) signals, is constructed. The algorithm for multidimensional nonlinear filtering of MP signals is synthesized, realizing the statistical redundancy of the MDI to compensate for the noise stability losses caused by the use of MP signals.

Extraction of Dems and Orthoimages from Archive Aerial Imagery to Support Project Planning in Civil Engineering

Science.gov (United States)

Cogliati, M.; Tonelli, E.; Battaglia, D.; Scaioni, M.

2017-12-01

Archive aerial photos represent a valuable heritage to provide information about land content and topography in the past years. Today, the availability of low-cost and open-source solutions for photogrammetric processing of close-range and drone images offers the chance to provide outputs such as DEM's and orthoimages in easy way. This paper is aimed at demonstrating somehow and to which level of accuracy digitized archive aerial photos may be used within a such kind of low-cost software (Agisoft Photoscan Professional®) to generate photogrammetric outputs. Different steps of the photogrammetric processing workflow are presented and discussed. The main conclusion is that this procedure may come to provide some final products, which however do not feature the high accuracy and resolution that may be obtained using high-end photogrammetric software packages specifically designed for aerial survey projects. In the last part a case study is presented about the use of four-epoch archive of aerial images to analyze the area where a tunnel has to be excavated.

EXTRACTION OF DEMS AND ORTHOIMAGES FROM ARCHIVE AERIAL IMAGERY TO SUPPORT PROJECT PLANNING IN CIVIL ENGINEERING

Directory of Open Access Journals (Sweden)

M. Cogliati

2017-12-01

Full Text Available Archive aerial photos represent a valuable heritage to provide information about land content and topography in the past years. Today, the availability of low-cost and open-source solutions for photogrammetric processing of close-range and drone images offers the chance to provide outputs such as DEM’s and orthoimages in easy way. This paper is aimed at demonstrating somehow and to which level of accuracy digitized archive aerial photos may be used within a such kind of low-cost software (Agisoft Photoscan Professional® to generate photogrammetric outputs. Different steps of the photogrammetric processing workflow are presented and discussed. The main conclusion is that this procedure may come to provide some final products, which however do not feature the high accuracy and resolution that may be obtained using high-end photogrammetric software packages specifically designed for aerial survey projects. In the last part a case study is presented about the use of four-epoch archive of aerial images to analyze the area where a tunnel has to be excavated.

Control of a Quadcopter Aerial Robot Using Optic Flow Sensing

Science.gov (United States)

Hurd, Michael Brandon

This thesis focuses on the motion control of a custom-built quadcopter aerial robot using optic flow sensing. Optic flow sensing is a vision-based approach that can provide a robot the ability to fly in global positioning system (GPS) denied environments, such as indoor environments. In this work, optic flow sensors are used to stabilize the motion of quadcopter robot, where an optic flow algorithm is applied to provide odometry measurements to the quadcopter's central processing unit to monitor the flight heading. The optic-flow sensor and algorithm are capable of gathering and processing the images at 250 frames/sec, and the sensor package weighs 2.5 g and has a footprint of 6 cm2 in area. The odometry value from the optic flow sensor is then used a feedback information in a simple proportional-integral-derivative (PID) controller on the quadcopter. Experimental results are presented to demonstrate the effectiveness of using optic flow for controlling the motion of the quadcopter aerial robot. The technique presented herein can be applied to different types of aerial robotic systems or unmanned aerial vehicles (UAVs), as well as unmanned ground vehicles (UGV).

An Application of Computer Vision Systems to Solve the Problem of Unmanned Aerial Vehicle Control

Directory of Open Access Journals (Sweden)

Aksenov Alexey Y.

2014-09-01

Full Text Available The paper considers an approach for application of computer vision systems to solve the problem of unmanned aerial vehicle control. The processing of images obtained through onboard camera is required for absolute positioning of aerial platform (automatic landing and take-off, hovering etc. used image processing on-board camera. The proposed method combines the advantages of existing systems and gives the ability to perform hovering over a given point, the exact take-off and landing. The limitations of implemented methods are determined and the algorithm is proposed to combine them in order to improve the efficiency.

Individual Building Rooftop and Tree Crown Segmentation from High-Resolution Urban Aerial Optical Images

Directory of Open Access Journals (Sweden)

Jichao Jiao

2016-01-01

Full Text Available We segment buildings and trees from aerial photographs by using superpixels, and we estimate the tree’s parameters by using a cost function proposed in this paper. A method based on image complexity is proposed to refine superpixels boundaries. In order to classify buildings from ground and classify trees from grass, the salient feature vectors that include colors, Features from Accelerated Segment Test (FAST corners, and Gabor edges are extracted from refined superpixels. The vectors are used to train the classifier based on Naive Bayes classifier. The trained classifier is used to classify refined superpixels as object or nonobject. The properties of a tree, including its locations and radius, are estimated by minimizing the cost function. The shadow is used to calculate the tree height using sun angle and the time when the image was taken. Our segmentation algorithm is compared with other two state-of-the-art segmentation algorithms, and the tree parameters obtained in this paper are compared to the ground truth data. Experiments show that the proposed method can segment trees and buildings appropriately, yielding higher precision and better recall rates, and the tree parameters are in good agreement with the ground truth data.

Implementation of an IMU Aided Image Stacking Algorithm in a Digital Camera for Unmanned Aerial Vehicles

Directory of Open Access Journals (Sweden)

Ahmad Audi

2017-07-01

Full Text Available Images acquired with a long exposure time using a camera embedded on UAVs (Unmanned Aerial Vehicles exhibit motion blur due to the erratic movements of the UAV. The aim of the present work is to be able to acquire several images with a short exposure time and use an image processing algorithm to produce a stacked image with an equivalent long exposure time. Our method is based on the feature point image registration technique. The algorithm is implemented on the light-weight IGN (Institut national de l’information géographique camera, which has an IMU (Inertial Measurement Unit sensor and an SoC (System on Chip/FPGA (Field-Programmable Gate Array. To obtain the correct parameters for the resampling of the images, the proposed method accurately estimates the geometrical transformation between the first and the N-th images. Feature points are detected in the first image using the FAST (Features from Accelerated Segment Test detector, then homologous points on other images are obtained by template matching using an initial position benefiting greatly from the presence of the IMU sensor. The SoC/FPGA in the camera is used to speed up some parts of the algorithm in order to achieve real-time performance as our ultimate objective is to exclusively write the resulting image to save bandwidth on the storage device. The paper includes a detailed description of the implemented algorithm, resource usage summary, resulting processing time, resulting images and block diagrams of the described architecture. The resulting stacked image obtained for real surveys does not seem visually impaired. An interesting by-product of this algorithm is the 3D rotation estimated by a photogrammetric method between poses, which can be used to recalibrate in real time the gyrometers of the IMU. Timing results demonstrate that the image resampling part of this algorithm is the most demanding processing task and should also be accelerated in the FPGA in future work.

Implementation of an IMU Aided Image Stacking Algorithm in a Digital Camera for Unmanned Aerial Vehicles.

Science.gov (United States)

Audi, Ahmad; Pierrot-Deseilligny, Marc; Meynard, Christophe; Thom, Christian

2017-07-18

Images acquired with a long exposure time using a camera embedded on UAVs (Unmanned Aerial Vehicles) exhibit motion blur due to the erratic movements of the UAV. The aim of the present work is to be able to acquire several images with a short exposure time and use an image processing algorithm to produce a stacked image with an equivalent long exposure time. Our method is based on the feature point image registration technique. The algorithm is implemented on the light-weight IGN (Institut national de l'information géographique) camera, which has an IMU (Inertial Measurement Unit) sensor and an SoC (System on Chip)/FPGA (Field-Programmable Gate Array). To obtain the correct parameters for the resampling of the images, the proposed method accurately estimates the geometrical transformation between the first and the N -th images. Feature points are detected in the first image using the FAST (Features from Accelerated Segment Test) detector, then homologous points on other images are obtained by template matching using an initial position benefiting greatly from the presence of the IMU sensor. The SoC/FPGA in the camera is used to speed up some parts of the algorithm in order to achieve real-time performance as our ultimate objective is to exclusively write the resulting image to save bandwidth on the storage device. The paper includes a detailed description of the implemented algorithm, resource usage summary, resulting processing time, resulting images and block diagrams of the described architecture. The resulting stacked image obtained for real surveys does not seem visually impaired. An interesting by-product of this algorithm is the 3D rotation estimated by a photogrammetric method between poses, which can be used to recalibrate in real time the gyrometers of the IMU. Timing results demonstrate that the image resampling part of this algorithm is the most demanding processing task and should also be accelerated in the FPGA in future work.

Interesting images: Multiple coronary artery aneurysms.

Science.gov (United States)

Howard, Jonathon M; Viswanath, Omar; Armas, Alfredo; Santana, Orlando; Rosen, Gerald P

2017-01-01

We present the case of a 65-year-old male who presented with stable angina and dyspnea on exertion. His initial workup yielded a positive treadmill stress test for reversible apical ischemia, and transthoracic echocardiogram demonstrated impaired systolic function. Cardiac catheterization was then performed, revealing severe atherosclerotic disease including multiple coronary artery aneurysms. As a result, the patient was advised to and subsequently underwent a coronary artery bypass graft. This case highlights the presence of multiple coronary artery aneurysms and the ability to appreciate these pathologic findings on multiple imaging modalities, including coronary angiogram, transesophageal echocardiography, and direct visualization through the surgical field.

Multiple event 2D image intensifier scintillation detector

International Nuclear Information System (INIS)

Thieberger, P.; Wegner, H.E.; Lee, R.C.

1981-01-01

An image intensifier scintillation detector has been developed for the simultaneous detection of multiple light or heavy ions down to very low energies. The relative X-Y positions of each ion are read out by digitization of a television image of the light amplified scintillations. The maximum data rate is limited by the present television scan speed to 15 multiple events per second and to about one event second by the microcomputer presently used to store and process the data. (orig.)

ULTRAMAP V3 – A REVOLUTION IN AERIAL PHOTOGRAMMETRY

Directory of Open Access Journals (Sweden)

B. Reitinger

2012-07-01

Full Text Available In the last years, Microsoft has driven innovation in the aerial photogrammetry community. Besides the market leading camera technology, UltraMap has grown to an outstanding photogrammetric workflow system which enables users to effectively work with large digital aerial image blocks in a highly automated way. Best example is the project-based color balancing approach which automatically balances images to a homogeneous block. UltraMap V3 continues innovation, and offers a revolution in terms of ortho processing. A fully automated dense matching module strives for high precision digital surface models (DSMs which are calculated either on CPUs or on GPUs using a distributed processing framework. By applying constrained filtering algorithms, a digital terrain model can be derived which in turn can be used for fully automated traditional ortho texturing. By having the knowledge about the underlying geometry, seamlines can be generated automatically by applying cost functions in order to minimize visual disturbing artifacts. By exploiting the generated DSM information, a DSMOrtho is created using the balanced input images. Again, seamlines are detected automatically resulting in an automatically balanced ortho mosaic. Interactive block-based radiometric adjustments lead to a high quality ortho product based on UltraCam imagery. UltraMap v3 is the first fully integrated and interactive solution for supporting UltraCam images at best in order to deliver DSM and ortho imagery.

Can reliable sage-grouse lek counts be obtained using aerial infrared technology

Science.gov (United States)

Gillette, Gifford L.; Coates, Peter S.; Petersen, Steven; Romero, John P.

2013-01-01

More effective methods for counting greater sage-grouse (Centrocercus urophasianus) are needed to better assess population trends through enumeration or location of new leks. We describe an aerial infrared technique for conducting sage-grouse lek counts and compare this method with conventional ground-based lek count methods. During the breeding period in 2010 and 2011, we surveyed leks from fixed-winged aircraft using cryogenically cooled mid-wave infrared cameras and surveyed the same leks on the same day from the ground following a standard lek count protocol. We did not detect significant differences in lek counts between surveying techniques. These findings suggest that using a cryogenically cooled mid-wave infrared camera from an aerial platform to conduct lek surveys is an effective alternative technique to conventional ground-based methods, but further research is needed. We discuss multiple advantages to aerial infrared surveys, including counting in remote areas, representing greater spatial variation, and increasing the number of counted leks per season. Aerial infrared lek counts may be a valuable wildlife management tool that releases time and resources for other conservation efforts. Opportunities exist for wildlife professionals to refine and apply aerial infrared techniques to wildlife monitoring programs because of the increasing reliability and affordability of this technology.

Use of archive aerial photography for monitoring black mangrove populations

Science.gov (United States)

A study was conducted on the south Texas Gulf Coast to evaluate archive aerial color-infrared (CIR) photography combined with supervised image analysis techniques to quantify changes in black mangrove [Avicennia germinans (L.) L.] populations over a 26-year period. Archive CIR film from two study si...

Aerial photography flight quality assessment with GPS/INS and DEM data

Science.gov (United States)

Zhao, Haitao; Zhang, Bing; Shang, Jiali; Liu, Jiangui; Li, Dong; Chen, Yanyan; Zuo, Zhengli; Chen, Zhengchao

2018-01-01

The flight altitude, ground coverage, photo overlap, and other acquisition specifications of an aerial photography flight mission directly affect the quality and accuracy of the subsequent mapping tasks. To ensure smooth post-flight data processing and fulfill the pre-defined mapping accuracy, flight quality assessments should be carried out in time. This paper presents a novel and rigorous approach for flight quality evaluation of frame cameras with GPS/INS data and DEM, using geometric calculation rather than image analysis as in the conventional methods. This new approach is based mainly on the collinearity equations, in which the accuracy of a set of flight quality indicators is derived through a rigorous error propagation model and validated with scenario data. Theoretical analysis and practical flight test of an aerial photography mission using an UltraCamXp camera showed that the calculated photo overlap is accurate enough for flight quality assessment of 5 cm ground sample distance image, using the SRTMGL3 DEM and the POSAV510 GPS/INS data. An even better overlap accuracy could be achieved for coarser-resolution aerial photography. With this new approach, the flight quality evaluation can be conducted on site right after landing, providing accurate and timely information for decision making.

Autonomy of image and use of single or multiple sense modalities in original verbal image production.

Science.gov (United States)

Khatena, J

1978-06-01

The use of a single or of multiple sense modalities in the production of original verbal images as related to autonomy of imagery was explored. 72 college adults were administered Onomatopoeia and Images and the Gordon Test of Visual Imagery Control. A modified scoring procedure for the Gordon scale differentiated imagers who were moderate or low in autonomy. The two groups produced original verbal images using multiple sense modalities more frequently than a single modality.

Design and realization of an AEC&AGC system for the CCD aerial camera

Science.gov (United States)

Liu, Hai ying; Feng, Bing; Wang, Peng; Li, Yan; Wei, Hao yun

2015-08-01

An AEC and AGC(Automatic Exposure Control and Automatic Gain Control) system was designed for a CCD aerial camera with fixed aperture and electronic shutter. The normal AEC and AGE algorithm is not suitable to the aerial camera since the camera always takes high-resolution photographs in high-speed moving. The AEC and AGE system adjusts electronic shutter and camera gain automatically according to the target brightness and the moving speed of the aircraft. An automatic Gamma correction is used before the image is output so that the image is better for watching and analyzing by human eyes. The AEC and AGC system could avoid underexposure, overexposure, or image blurring caused by fast moving or environment vibration. A series of tests proved that the system meet the requirements of the camera system with its fast adjusting speed, high adaptability, high reliability in severe complex environment.

Earth analog image digitization of field, aerial, and lab experiment studies for Planetary Data System archiving.

Science.gov (United States)

Williams, D. A.; Nelson, D. M.

2017-12-01

A portion of the earth analog image archive at the Ronald Greeley Center for Planetary Studies (RGCPS)-the NASA Regional Planetary Information Facility at Arizona State University-is being digitized and will be added to the Planetary Data System (PDS) for public use. This will be a first addition of terrestrial data to the PDS specifically for comparative planetology studies. Digitization is separated into four tasks. First is the scanning of aerial photographs of volcanic and aeolian structures and flows. The second task is to scan field site images taken from ground and low-altitude aircraft of volcanic structures, lava flows, lava tubes, dunes, and wind streaks. The third image set to be scanned includes photographs of lab experiments from the NASA Planetary Aeolian Laboratory wind tunnels, vortex generator, and of wax models. Finally, rare NASA documents are being scanned and formatted as PDF files. Thousands of images are to be scanned for this project. Archiving of the data will follow the PDS4 standard, where the entire project is classified as a single bundle, with individual subjects (i.e., the Amboy Crater volcanic structure in the Mojave Desert of California) as collections. Within the collections, each image is considered a product, with a unique ID and associated XML document. Documents describing the image data, including the subject and context, will be included with each collection. Once complete, the data will be hosted by a PDS data node and available for public search and download. As one of the first earth analog datasets to be archived by the PDS, this project could prompt the digitizing and making available of historic datasets from other facilities for the scientific community.

Geodetic glacier mass balances at the push of a button: application of Structure from Motion technology on aerial images in mountain regions

Science.gov (United States)

Bolch, T.; Mölg, N.

2017-12-01

The application of Structure-from-Motion (SfM) to generate digital terrain models (DTMs) derived out of images from various kinds of sources has strongly increased in recent years. The major reason for this is its easy-to-use handling in comparison to conventional photogrammetry. In glaciology, DTMs are intensely used, among others, to calculate the geodetic mass balances. Few studies investigated the application of SfM to aerial images in mountainous terrain and results look promising. We tested this technique in a demanding environment in the Swiss Alps including very steep slopes, snow and ice covered terrain. SfM (using the commercial software packages of Agisoft Photoscan and Pix4DMapper) and conventional photogrammetry (ERDAS Photogrammetry) were applied on archival aerial images for nine dates between 1946 and 2005 the results were compared regarding bundle adjustment and final DTM quality. The overall precision of the DTMs could be defined with the use of a modern, high-quality reference DTM by Swisstopo. Results suggest a high performance of SfM to produce DTMs of similar quality as conventional photogrammetry. A ground resolution of high quality (little noise and artefacts) can be up to 50% higher, with 3-6 times less user effort. However, the controls on the commercial SfM software packages are limited in comparison to ERDAS Photogrammetry. SfM performs less reliably when few images with little overlap are processed. Overall, the uncertainty of DTMs from the different software are comparable and mostly within the uncertainty range of the reference DTM, making them highly valuable for glaciological purposes. Even though SfM facilitates the largely automated production of high quality DTMs, the user is not exempt from a thorough quality check, at best with reference data where available.

AMRMS Aerial survey database

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — An aerial monitoring program was conducted during the period 1962 - 2003 in cooperation with aerial spotters working for the commercial purse seine fleet. Flights...

Autonomous Aerial Refueling Ground Test Demonstration—A Sensor-in-the-Loop, Non-Tracking Method

Directory of Open Access Journals (Sweden)

Chao-I Chen

2015-05-01

Full Text Available An essential capability for an unmanned aerial vehicle (UAV to extend its airborne duration without increasing the size of the aircraft is called the autonomous aerial refueling (AAR. This paper proposes a sensor-in-the-loop, non-tracking method for probe-and-drogue style autonomous aerial refueling tasks by combining sensitivity adjustments of a 3D Flash LIDAR camera with computer vision based image-processing techniques. The method overcomes the inherit ambiguity issues when reconstructing 3D information from traditional 2D images by taking advantage of ready to use 3D point cloud data from the camera, followed by well-established computer vision techniques. These techniques include curve fitting algorithms and outlier removal with the random sample consensus (RANSAC algorithm to reliably estimate the drogue center in 3D space, as well as to establish the relative position between the probe and the drogue. To demonstrate the feasibility of the proposed method on a real system, a ground navigation robot was designed and fabricated. Results presented in the paper show that using images acquired from a 3D Flash LIDAR camera as real time visual feedback, the ground robot is able to track a moving simulated drogue and continuously narrow the gap between the robot and the target autonomously.

Mapping Reflectance Anisotropy of a Potato Canopy Using Aerial Images Acquired with an Unmanned Aerial Vehicle

Directory of Open Access Journals (Sweden)

Peter P. J. Roosjen

2017-04-01

Full Text Available Viewing and illumination geometry has a strong influence on optical measurements of natural surfaces due to their anisotropic reflectance properties. Typically, cameras on-board unmanned aerial vehicles (UAVs are affected by this because of their relatively large field of view (FOV and thus large range of viewing angles. In this study, we investigated the magnitude of reflectance anisotropy effects in the 500–900 nm range, captured by a frame camera mounted on a UAV during a standard mapping flight. After orthorectification and georeferencing of the images collected by the camera, we calculated the viewing geometry of all observations of each georeferenced ground pixel, forming a dataset with multi-angular observations. We performed UAV flights on two days during the summer of 2016 over an experimental potato field where different zones in the field received different nitrogen fertilization treatments. These fertilization levels caused variation in potato plant growth and thereby differences in structural properties such as leaf area index (LAI and canopy cover. We fitted the Rahman–Pinty–Verstraete (RPV model through the multi-angular observations of each ground pixel to quantify, interpret, and visualize the anisotropy patterns in our study area. The Θ parameter of the RPV model, which controls the proportion of forward and backward scattering, showed strong correlation with canopy cover, where in general an increase in canopy cover resulted in a reduction of backward scattering intensity, indicating that reflectance anisotropy contains information on canopy structure. In this paper, we demonstrated that anisotropy data can be extracted from measurements using a frame camera, collected during a typical UAV mapping flight. Future research will focus on how to use the anisotropy signal as a source of information for estimation of physical vegetation properties.

Publishing WWII aerial photographs in geographical and library information systems

NARCIS (Netherlands)

Verhelst, E.C.H.; Missel, L.; Vanmeulebrouk, B.; Rip, F.I.

2012-01-01

The Library of the Dutch Wageningen University and Research centre houses a collection of aerial photographs taken by the Allied Air Forces. The collection is part of a project that aims to publish these images in a user friendly way so that they are accessible to a wide audience. This paper

An efficient multiple exposure image fusion in JPEG domain

Science.gov (United States)

Hebbalaguppe, Ramya; Kakarala, Ramakrishna

2012-01-01

In this paper, we describe a method to fuse multiple images taken with varying exposure times in the JPEG domain. The proposed algorithm finds its application in HDR image acquisition and image stabilization for hand-held devices like mobile phones, music players with cameras, digital cameras etc. Image acquisition at low light typically results in blurry and noisy images for hand-held camera's. Altering camera settings like ISO sensitivity, exposure times and aperture for low light image capture results in noise amplification, motion blur and reduction of depth-of-field respectively. The purpose of fusing multiple exposures is to combine the sharp details of the shorter exposure images with high signal-to-noise-ratio (SNR) of the longer exposure images. The algorithm requires only a single pass over all images, making it efficient. It comprises of - sigmoidal boosting of shorter exposed images, image fusion, artifact removal and saturation detection. Algorithm does not need more memory than a single JPEG macro block to be kept in memory making it feasible to be implemented as the part of a digital cameras hardware image processing engine. The Artifact removal step reuses the JPEGs built-in frequency analysis and hence benefits from the considerable optimization and design experience that is available for JPEG.

Random Access Memories: A New Paradigm for Target Detection in High Resolution Aerial Remote Sensing Images.

Science.gov (United States)

Zou, Zhengxia; Shi, Zhenwei

2018-03-01

We propose a new paradigm for target detection in high resolution aerial remote sensing images under small target priors. Previous remote sensing target detection methods frame the detection as learning of detection model + inference of class-label and bounding-box coordinates. Instead, we formulate it from a Bayesian view that at inference stage, the detection model is adaptively updated to maximize its posterior that is determined by both training and observation. We call this paradigm "random access memories (RAM)." In this paradigm, "Memories" can be interpreted as any model distribution learned from training data and "random access" means accessing memories and randomly adjusting the model at detection phase to obtain better adaptivity to any unseen distribution of test data. By leveraging some latest detection techniques e.g., deep Convolutional Neural Networks and multi-scale anchors, experimental results on a public remote sensing target detection data set show our method outperforms several other state of the art methods. We also introduce a new data set "LEarning, VIsion and Remote sensing laboratory (LEVIR)", which is one order of magnitude larger than other data sets of this field. LEVIR consists of a large set of Google Earth images, with over 22 k images and 10 k independently labeled targets. RAM gives noticeable upgrade of accuracy (an mean average precision improvement of 1% ~ 4%) of our baseline detectors with acceptable computational overhead.

Detecting blind building façades from highly overlapping wide angle aerial imagery

Science.gov (United States)

Burochin, Jean-Pascal; Vallet, Bruno; Brédif, Mathieu; Mallet, Clément; Brosset, Thomas; Paparoditis, Nicolas

2014-10-01

This paper deals with the identification of blind building façades, i.e. façades which have no openings, in wide angle aerial images with a decimeter pixel size, acquired by nadir looking cameras. This blindness characterization is in general crucial for real estate estimation and has, at least in France, a particular importance on the evaluation of legal permission of constructing on a parcel due to local urban planning schemes. We assume that we have at our disposal an aerial survey with a relatively high stereo overlap along-track and across-track and a 3D city model of LoD 1, that can have been generated with the input images. The 3D model is textured with the aerial imagery by taking into account the 3D occlusions and by selecting for each façade the best available resolution texture seeing the whole façade. We then parse all 3D façades textures by looking for evidence of openings (windows or doors). This evidence is characterized by a comprehensive set of basic radiometric and geometrical features. The blindness prognostic is then elaborated through an (SVM) supervised classification. Despite the relatively low resolution of the images, we reach a classification accuracy of around 85% on decimeter resolution imagery with 60 × 40 % stereo overlap. On the one hand, we show that the results are very sensitive to the texturing resampling process and to vegetation presence on façade textures. On the other hand, the most relevant features for our classification framework are related to texture uniformity and horizontal aspect and to the maximal contrast of the opening detections. We conclude that standard aerial imagery used to build 3D city models can also be exploited to some extent and at no additional cost for facade blindness characterisation.

a Modified Projective Transformation Scheme for Mosaicking Multi-Camera Imaging System Equipped on a Large Payload Fixed-Wing Uas

Science.gov (United States)

Jhan, J. P.; Li, Y. T.; Rau, J. Y.

2015-03-01

In recent years, Unmanned Aerial System (UAS) has been applied to collect aerial images for mapping, disaster investigation, vegetation monitoring and etc. It is a higher mobility and lower risk platform for human operation, but the low payload and short operation time reduce the image collection efficiency. In this study, one nadir and four oblique consumer grade DSLR cameras composed multiple camera system is equipped on a large payload UAS, which is designed to collect large ground coverage images in an effective way. The field of view (FOV) is increased to 127 degree, which is thus suitable to collect disaster images in mountainous area. The synthetic acquired five images are registered and mosaicked as larger format virtual image for reducing the number of images, post processing time, and for easier stereo plotting. Instead of traditional image matching and applying bundle adjustment method to estimate transformation parameters, the IOPs and ROPs of multiple cameras are calibrated and derived the coefficients of modified projective transformation (MPT) model for image mosaicking. However, there are some uncertainty of indoor calibrated IOPs and ROPs since the different environment conditions as well as the vibration of UAS, which will cause misregistration effect of initial MPT results. Remaining residuals are analysed through tie points matching on overlapping area of initial MPT results, in which displacement and scale difference are introduced and corrected to modify the ROPs and IOPs for finer registration results. In this experiment, the internal accuracy of mosaic image is better than 0.5 pixels after correcting the systematic errors. Comparison between separate cameras and mosaic images through rigorous aerial triangulation are conducted, in which the RMSE of 5 control and 9 check points is less than 5 cm and 10 cm in planimetric and vertical directions, respectively, for all cases. It proves that the designed imaging system and the proposed scheme

A MODIFIED PROJECTIVE TRANSFORMATION SCHEME FOR MOSAICKING MULTI-CAMERA IMAGING SYSTEM EQUIPPED ON A LARGE PAYLOAD FIXED-WING UAS

Directory of Open Access Journals (Sweden)

J. P. Jhan

2015-03-01

Full Text Available In recent years, Unmanned Aerial System (UAS has been applied to collect aerial images for mapping, disaster investigation, vegetation monitoring and etc. It is a higher mobility and lower risk platform for human operation, but the low payload and short operation time reduce the image collection efficiency. In this study, one nadir and four oblique consumer grade DSLR cameras composed multiple camera system is equipped on a large payload UAS, which is designed to collect large ground coverage images in an effective way. The field of view (FOV is increased to 127 degree, which is thus suitable to collect disaster images in mountainous area. The synthetic acquired five images are registered and mosaicked as larger format virtual image for reducing the number of images, post processing time, and for easier stereo plotting. Instead of traditional image matching and applying bundle adjustment method to estimate transformation parameters, the IOPs and ROPs of multiple cameras are calibrated and derived the coefficients of modified projective transformation (MPT model for image mosaicking. However, there are some uncertainty of indoor calibrated IOPs and ROPs since the different environment conditions as well as the vibration of UAS, which will cause misregistration effect of initial MPT results. Remaining residuals are analysed through tie points matching on overlapping area of initial MPT results, in which displacement and scale difference are introduced and corrected to modify the ROPs and IOPs for finer registration results. In this experiment, the internal accuracy of mosaic image is better than 0.5 pixels after correcting the systematic errors. Comparison between separate cameras and mosaic images through rigorous aerial triangulation are conducted, in which the RMSE of 5 control and 9 check points is less than 5 cm and 10 cm in planimetric and vertical directions, respectively, for all cases. It proves that the designed imaging system and the

Development of an image intensifier-TV digital imaging system with a multiple-slit scanning x-ray beam

International Nuclear Information System (INIS)

Kume, Y.; Doi, K.

1986-01-01

The authors are developing a new digital x-ray imaging system employing a multiple-slit assembly (MSA) and an image intensifier (II)-TV digital system. The final image consisting of primary radiation is digitally reconstructed from multiple slit images obtained with the MSA. This system can significantly reduce the scattered radiation from an object and the veiling glare from II-TV system. The quality of the reconstructed image is related to many parameters, such as slit width, the number of image frames, and the image reconstruction algorithm. They present the effect of these various parameters on basic imaging properties and the practicability of the method in comparison with conventional wide beam imaging

Challenges of Integrating Unmanned Aerial Vehicles In Civil Application

International Nuclear Information System (INIS)

Eid, B M; Albatsh, F; Faris, W F; Chebil, J

2013-01-01

Unmanned Aerial Vehicle (UAV) has evolved rapidly over the past decade. There have been an increased number of studies aiming at improving UAV and in its use for different civil applications. This paper highlights the fundamentals of UAV system and examines the challenges related with the major components such as motors, drives, power systems, communication systems and image processing tools and equipment

Increasing Benefit of Magnetic Resonance Imaging in Multiple Sclerosis

International Nuclear Information System (INIS)

Pyhtinen, J.; Karttunen, A.; Tikkakoski, T.

2006-01-01

Magnetic resonance imaging (MRI) has emerged as an essential tool of multiple sclerosis (MS) diagnosis and has opened up completely new prospects in MS research and treatment trials. It is a sensitive method that gives direct evidence of tissue pathology and has greatly increased our knowledge of MS. In clinical work, MRI is used to confirm and exclude the diagnosis of MS. The international recommendation is that every suspected MS patient should undergo at least one brain MRI. T2-weighted images are the standard tool in clinical work, and functional imaging methods are mainly used in MS research. The subtypes and the course of the disease cause variation in MRI findings. Here, we present a general overview of MR findings in MS. Brain, magnetic resonance imaging, multiple sclerosis, spinal cord

Increasing Benefit of Magnetic Resonance Imaging in Multiple Sclerosis

Energy Technology Data Exchange (ETDEWEB)

Pyhtinen, J.; Karttunen, A.; Tikkakoski, T. [Radiologian Klinikka, Oulu (Finland)

2006-11-15

Magnetic resonance imaging (MRI) has emerged as an essential tool of multiple sclerosis (MS) diagnosis and has opened up completely new prospects in MS research and treatment trials. It is a sensitive method that gives direct evidence of tissue pathology and has greatly increased our knowledge of MS. In clinical work, MRI is used to confirm and exclude the diagnosis of MS. The international recommendation is that every suspected MS patient should undergo at least one brain MRI. T2-weighted images are the standard tool in clinical work, and functional imaging methods are mainly used in MS research. The subtypes and the course of the disease cause variation in MRI findings. Here, we present a general overview of MR findings in MS. Brain, magnetic resonance imaging, multiple sclerosis, spinal cord.

a Method for Simultaneous Aerial and Terrestrial Geodata Acquisition for Corridor Mapping

Science.gov (United States)

Molina, P.; Blázquez, M.; Sastre, J.; Colomina, I.

2015-08-01

In this paper, we present mapKITE, a new mobile, simultaneous terrestrial and aerial, geodata collection and post-processing method. On one side, the method combines a terrestrial mobile mapping system (TMMS) with an unmanned aerial mapping one, both equipped with remote sensing payloads (at least, a nadir-looking visible-band camera in the UA) by means of which aerial and terrestrial geodata are acquired simultaneously. This tandem geodata acquisition system is based on a terrestrial vehicle (TV) and on an unmanned aircraft (UA) linked by a 'virtual tether', that is, a mechanism based on the real-time supply of UA waypoints by the TV. By means of the TV-to-UA tether, the UA follows the TV keeping a specific relative TV-to-UA spatial configuration enabling the simultaneous operation of both systems to obtain highly redundant and complementary geodata. On the other side, mapKITE presents a novel concept for geodata post-processing favoured by the rich geometrical aspects derived from the mapKITE tandem simultaneous operation. The approach followed for sensor orientation and calibration of the aerial images captured by the UA inherits the principles of Integrated Sensor Orientation (ISO) and adds the pointing-and-scaling photogrammetric measurement of a distinctive element observed in every UA image, which is a coded target mounted on the roof of the TV. By means of the TV navigation system, the orientation of the TV coded target is performed and used in the post-processing UA image orientation approach as a Kinematic Ground Control Point (KGCP). The geometric strength of a mapKITE ISO network is therefore high as it counts with the traditional tie point image measurements, static ground control points, kinematic aerial control and the new point-and-scale measurements of the KGCPs. With such a geometry, reliable system and sensor orientation and calibration and eventual further reduction of the number of traditional ground control points is feasible. The different

THE DRONES ARE COMING. WHAT TO CHOOSE? LOW AND MEDIUM ALTITUDE AERIAL ARCHAEOLOGY ON LIMES TRANSALUTANUS.

Directory of Open Access Journals (Sweden)

Dan Ștefan

2016-07-01

Full Text Available Aerial archaeology has more than one century of tradition as a valuable research method. Like archaeology, the aerial reconnaissance is undergoing dynamic changes. The field must face the profound conceptual challenges raised by the evolving demands of archaeologists. In addition, aerial archaeology has to adapt its own methods in order to constantly incorporate new technologies such as: thermal vision, LiDAR and also advanced photogrammetry processing techniques. One of the greatest challenges and promising perspectives for evolution of the field is the arriving and rapidly spreading of small remote controlled aerial vehicles (UAVs – Unmanned Aerial Vehicles, known also as drones. However, a real development of the UAVs based aerial archaeology’s branch is conditioned by the availability of special tailored aerial vehicles for archaeologist’s needs. Unfortunately, the time has not yet come for this, while the major efforts in drones development is spent for aerial videography applications, surveillance and general entertainment. The implementation of a research project, dedicated to the longest built sector of the Roman limes in Dacia – Limes Transalutanus, represented for the authors a suitable occasion to assess the possibilities and limits of the large scale aerial archaeology based on UAVs. On the occasion there were tested two custom flying platforms and one commercial, multiple flight strategies and several processing algorithms. The linear nature and the extent of the site (basically a corridor of 157 km in length called for distinct augmentation of equipment and survey workflows, with applicability in ‘corridor’ archaeological projects like those for highways and utilities networks.

Optical multiple-image encryption based on multiplane phase retrieval and interference

International Nuclear Information System (INIS)

Chen, Wen; Chen, Xudong

2011-01-01

In this paper, we propose a new method for optical multiple-image encryption based on multiplane phase retrieval and interference. An optical encoding system is developed in the Fresnel domain. A phase-only map is iteratively extracted based on a multiplane phase retrieval algorithm, and multiple plaintexts are simultaneously encrypted. Subsequently, the extracted phase-only map is further encrypted into two phase-only masks based on a non-iterative interference algorithm. During image decryption, the advantages and security of the proposed optical cryptosystem are analyzed. Numerical results are presented to demonstrate the validity of the proposed optical multiple-image encryption method

Assessment of Photogrammetric Mapping Accuracy Based on Variation Flying Altitude Using Unmanned Aerial Vehicle

International Nuclear Information System (INIS)

Udin, W S; Ahmad, A

2014-01-01

Photogrammetry is the earliest technique used to collect data for topographic mapping. The recent development in aerial photogrammetry is the used of large format digital aerial camera for producing topographic map. The aerial photograph can be in the form of metric or non-metric imagery. The cost of mapping using aerial photogrammetry is very expensive. In certain application, there is a need to map small area with limited budget. Due to the development of technology, small format aerial photogrammetry technology has been introduced and offers many advantages. Currently, digital map can be extracted from digital aerial imagery of small format camera mounted on light weight platform such as unmanned aerial vehicle (UAV). This study utilizes UAV system for large scale stream mapping. The first objective of this study is to investigate the use of light weight rotary-wing UAV for stream mapping based on different flying height. Aerial photograph were acquired at 60% forward lap and 30% sidelap specifications. Ground control points and check points were established using Total Station technique. The digital camera attached to the UAV was calibrated and the recovered camera calibration parameters were then used in the digital images processing. The second objective is to determine the accuracy of the photogrammetric output. In this study, the photogrammetric output such as stereomodel in three dimensional (3D), contour lines, digital elevation model (DEM) and orthophoto were produced from a small stream of 200m long and 10m width. The research output is evaluated for planimetry and vertical accuracy using root mean square error (RMSE). Based on the finding, sub-meter accuracy is achieved and the RMSE value decreases as the flying height increases. The difference is relatively small. Finally, this study shows that UAV is very useful platform for obtaining aerial photograph and subsequently used for photogrammetric mapping and other applications

Spinal focal lesion detection in multiple myeloma using multimodal image features

Science.gov (United States)

Fränzle, Andrea; Hillengass, Jens; Bendl, Rolf

2015-03-01

Multiple myeloma is a tumor disease in the bone marrow that affects the skeleton systemically, i.e. multiple lesions can occur in different sites in the skeleton. To quantify overall tumor mass for determining degree of disease and for analysis of therapy response, volumetry of all lesions is needed. Since the large amount of lesions in one patient impedes manual segmentation of all lesions, quantification of overall tumor volume is not possible until now. Therefore development of automatic lesion detection and segmentation methods is necessary. Since focal tumors in multiple myeloma show different characteristics in different modalities (changes in bone structure in CT images, hypointensity in T1 weighted MR images and hyperintensity in T2 weighted MR images), multimodal image analysis is necessary for the detection of focal tumors. In this paper a pattern recognition approach is presented that identifies focal lesions in lumbar vertebrae based on features from T1 and T2 weighted MR images. Image voxels within bone are classified using random forests based on plain intensities and intensity value derived features (maximum, minimum, mean, median) in a 5 x 5 neighborhood around a voxel from both T1 and T2 weighted MR images. A test data sample of lesions in 8 lumbar vertebrae from 4 multiple myeloma patients can be classified at an accuracy of 95% (using a leave-one-patient-out test). The approach provides a reasonable delineation of the example lesions. This is an important step towards automatic tumor volume quantification in multiple myeloma.

Effect of multiple circular holes Fraunhofer diffraction for the infrared optical imaging

Science.gov (United States)

Lu, Chunlian; Lv, He; Cao, Yang; Cai, Zhisong; Tan, Xiaojun

2014-11-01

With the development of infrared optics, infrared optical imaging systems play an increasingly important role in modern optical imaging systems. Infrared optical imaging is used in industry, agriculture, medical, military and transportation. But in terms of infrared optical imaging systems which are exposed for a long time, some contaminations will affect the infrared optical imaging. When the contamination contaminate on the lens surface of the optical system, it would affect diffraction. The lens can be seen as complementary multiple circular holes screen happen Fraunhofer diffraction. According to Babinet principle, you can get the diffraction of the imaging system. Therefore, by studying the multiple circular holes Fraunhofer diffraction, conclusions can be drawn about the effect of infrared imaging. This paper mainly studies the effect of multiple circular holes Fraunhofer diffraction for the optical imaging. Firstly, we introduce the theory of Fraunhofer diffraction and Point Spread Function. Point Spread Function is a basic tool to evaluate the image quality of the optical system. Fraunhofer diffraction will affect Point Spread Function. Then, the results of multiple circular holes Fraunhofer diffraction are given for different hole size and hole spacing. We choose the hole size from 0.1mm to 1mm and hole spacing from 0.3mm to 0.8mm. The infrared wavebands of optical imaging are chosen from 1μm to 5μm. We use the MATLAB to simulate light intensity distribution of multiple circular holes Fraunhofer diffraction. Finally, three-dimensional diffraction maps of light intensity are given to contrast.

Imaging moving objects from multiply scattered waves and multiple sensors

International Nuclear Information System (INIS)

Miranda, Analee; Cheney, Margaret

2013-01-01

In this paper, we develop a linearized imaging theory that combines the spatial, temporal and spectral components of multiply scattered waves as they scatter from moving objects. In particular, we consider the case of multiple fixed sensors transmitting and receiving information from multiply scattered waves. We use a priori information about the multipath background. We use a simple model for multiple scattering, namely scattering from a fixed, perfectly reflecting (mirror) plane. We base our image reconstruction and velocity estimation technique on a modification of a filtered backprojection method that produces a phase-space image. We plot examples of point-spread functions for different geometries and waveforms, and from these plots, we estimate the resolution in space and velocity. Through this analysis, we are able to identify how the imaging system depends on parameters such as bandwidth and number of sensors. We ultimately show that enhanced phase-space resolution for a distribution of moving and stationary targets in a multipath environment may be achieved using multiple sensors. (paper)

Aerial release of Rhinoncomimus latipes (Coleoptera: Curculionidae) to control Persicaria perfoliata (Polygonaceae) using an unmanned aerial system.

Science.gov (United States)

Park, Yong-Lak; Gururajan, Srikanth; Thistle, Harold; Chandran, Rakesh; Reardon, Richard

2018-01-01

Rhinoncomimus latipes (Coleoptera: Curculionidae) is a major biological control agent against the invasive plant Persicaria perfoliata. Release of R. latipes is challenging with the current visit-and-hand release approach because P. perfoliata shows a high degree of patchiness in the landscape, possesses recurved barbs on its stems, and often spreads into hard-to-access areas. This 3-year study developed and evaluated unmanned aerial systems (UAS) for precise aerial release of R. latipes to control P. perfoliata. We have developed two UAS (i.e. quad-rotor and tri-rotor) and an aerial release system to disseminate R. latipes. These include pods containing R. latipes and a dispenser to accommodate eight pods. Results of field tests to evaluate the systems showed no significant (P > 0.05) effects on survivorship and feeding ability of R. latipes after aerial release. Our study demonstrates the potential of UAS for precision aerial release of biological control agents to control invasive plants. The aerial deployment systems we have developed, including both pods and a dispenser, are low cost, logistically practical, and effective with no negative effects on aerially released R. latipes. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Superresolution Imaging Using Resonant Multiples and Plane-wave Migration Velocity Analysis

KAUST Repository

Guo, Bowen

2017-08-28

Seismic imaging is a technique that uses seismic echoes to map and detect underground geological structures. The conventional seismic image has the resolution limit of λ/2, where λ is the wavelength associated with the seismic waves propagating in the subsurface. To exceed this resolution limit, this thesis develops a new imaging method using resonant multiples, which produces superresolution images with twice or even more the spatial resolution compared to the conventional primary reflection image. A resonant multiple is defined as a seismic reflection that revisits the same subsurface location along coincident reflection raypath. This reverberated raypath is the reason for superresolution imaging because it increases the differences in reflection times associated with subtle changes in the spatial location of the reflector. For the practical implementation of superresolution imaging, I develop a post-stack migration technique that first enhances the signal-to-noise ratios (SNRs) of resonant multiples by a moveout-correction stacking method, and then migrates the post-stacked resonant multiples with the associated Kirchhoff or wave-equation migration formula. I show with synthetic and field data examples that the first-order resonant multiple image has about twice the spatial resolution compared to the primary reflection image. Besides resolution, the correct estimate of the subsurface velocity is crucial for determining the correct depth of reflectors. Towards this goal, wave-equation migration velocity analysis (WEMVA) is an image-domain method which inverts for the velocity model that maximizes the similarity of common image gathers (CIGs). Conventional WEMVA based on subsurface-offset, angle domain or time-lag CIGs requires significant computational and memory resources because it computes higher dimensional migration images in the extended image domain. To mitigate this problem, I present a new WEMVA method using plane-wave CIGs. Plane-wave CIGs reduce the

The linearized inversion of the generalized interferometric multiple imaging

KAUST Repository

Aldawood, Ali; Hoteit, Ibrahim; Alkhalifah, Tariq Ali

2016-01-01

such as vertical and nearly vertical fault planes, and salt flanks. To image first-order internal multiple, the GIMI framework consists of three datuming steps, followed by applying the zero-lag cross-correlation imaging condition. However, the standard GIMI

Mapping of a river using close range photogrammetry technique and unmanned aerial vehicle system

International Nuclear Information System (INIS)

Room, M H M; Ahmad, A

2014-01-01

Photogrammetry is a technique that can be used to record the information of any feature without direct contact. Nowadays, a combination of photogrammetry and Unmanned Aerial Vehicle (UAV) systems is widely used for various applications, especially for large scale mapping. UAV systems offer several advantages in terms of cost and image resolution compared to terrestrial photogrammetry and remote sensing system. Therefore, a combination of photogrammetry and UAV created a new term which is UAV photogrammetry. The aim of this study is to investigate the ability of a UAV system to map a river at very close distance. A digital camera is attached to the Hexacopter UAV and it is flown at 2 m above the ground surface to produce aerial photos. Then, the aerial photos are processed to create two photogrammetric products as output. These are mosaicked orthophoto and digital image. Both products are assessed (RSME). The RSME of X and Y coordinates are ±0.009 m and ±0.033 m respectively. As a conclusion, photogrammetry and the UAV system offer a reliable accuracy for mapping a river model and advantages in term of cost-efficient, high ground resolution and rapid data acquisition

Multiple-image hiding using super resolution reconstruction in high-frequency domains

Science.gov (United States)

Li, Xiao-Wei; Zhao, Wu-Xiang; Wang, Jun; Wang, Qiong-Hua

2017-12-01

In this paper, a robust multiple-image hiding method using the computer-generated integral imaging and the modified super-resolution reconstruction algorithm is proposed. In our work, the host image is first transformed into frequency domains by cellular automata (CA), to assure the quality of the stego-image, the secret images are embedded into the CA high-frequency domains. The proposed method has the following advantages: (1) robustness to geometric attacks because of the memory-distributed property of elemental images, (2) increasing quality of the reconstructed secret images as the scheme utilizes the modified super-resolution reconstruction algorithm. The simulation results show that the proposed multiple-image hiding method outperforms other similar hiding methods and is robust to some geometric attacks, e.g., Gaussian noise and JPEG compression attacks.

Multi-Model Estimation Based Moving Object Detection for Aerial Video

Directory of Open Access Journals (Sweden)

Yanning Zhang

2015-04-01

Full Text Available With the wide development of UAV (Unmanned Aerial Vehicle technology, moving target detection for aerial video has become a popular research topic in the computer field. Most of the existing methods are under the registration-detection framework and can only deal with simple background scenes. They tend to go wrong in the complex multi background scenarios, such as viaducts, buildings and trees. In this paper, we break through the single background constraint and perceive the complex scene accurately by automatic estimation of multiple background models. First, we segment the scene into several color blocks and estimate the dense optical flow. Then, we calculate an affine transformation model for each block with large area and merge the consistent models. Finally, we calculate subordinate degree to multi-background models pixel to pixel for all small area blocks. Moving objects are segmented by means of energy optimization method solved via Graph Cuts. The extensive experimental results on public aerial videos show that, due to multi background models estimation, analyzing each pixel’s subordinate relationship to multi models by energy minimization, our method can effectively remove buildings, trees and other false alarms and detect moving objects correctly.

Magnetic resonance imaging in multiple sclerosis

International Nuclear Information System (INIS)

Kesselring, J.; Ormerod, I.E.C.; Miller, D.H.; Du Boulay, G.H.; McDonald, W.I.

1989-01-01

In 1983 the Multiple Sclerosis Society of Great Britain and Northern Ireland set up the Multiple Sclerosis NMR Research Group at the Institute of Neurology and the National Hospital, Queen Square. The first aim of the Group was to define the role of MRI in the diagnosis and differential diagnosis of multiple sclerosis, and this Atlas represents a summary of that work. Our strategy was to determine the pattern of MRI abnormalities in clinically definite MS and to compare it with those of isolated clinical syndromes of the kind seen in MS (e.g. optic neuritis) and of other disorders with which MS can be confused clinically or radiologically. We have also been involved in a major program of experimental work designed to elucidate the origin of the abnormal signals in MRI. To describe this in full detail would go beyond the scope of the Atlas, but we have incorporated such results as far as they illuminate our clinical problems. The imager used was a 0.5 Tesla Picker superconducting system. Technical advances have been rapid since we began. Nevertheless, the quality of the images obtained at our relatively low field has enabled us to establish the patterns of abnormality in the brain in MS and the diseases which must be distinguished from it. (orig./MG)

A Mobile System for Measuring Water Surface Velocities Using Unmanned Aerial Vehicle and Large-Scale Particle Image Velocimetry

Science.gov (United States)

Chen, Y. L.

2015-12-01

Measurement technologies for velocity of river flow are divided into intrusive and nonintrusive methods. Intrusive method requires infield operations. The measuring process of intrusive methods are time consuming, and likely to cause damages of operator and instrument. Nonintrusive methods require fewer operators and can reduce instrument damages from directly attaching to the flow. Nonintrusive measurements may use radar or image velocimetry to measure the velocities at the surface of water flow. The image velocimetry, such as large scale particle image velocimetry (LSPIV) accesses not only the point velocity but the flow velocities in an area simultaneously. Flow properties of an area hold the promise of providing spatially information of flow fields. This study attempts to construct a mobile system UAV-LSPIV by using an unmanned aerial vehicle (UAV) with LSPIV to measure flows in fields. The mobile system consists of a six-rotor UAV helicopter, a Sony nex5T camera, a gimbal, an image transfer device, a ground station and a remote control device. The activate gimbal helps maintain the camera lens orthogonal to the water surface and reduce the extent of images being distorted. The image transfer device can monitor the captured image instantly. The operator controls the UAV by remote control device through ground station and can achieve the flying data such as flying height and GPS coordinate of UAV. The mobile system was then applied to field experiments. The deviation of velocities measured by UAV-LSPIV of field experiments and handhold Acoustic Doppler Velocimeter (ADV) is under 8%. The results of the field experiments suggests that the application of UAV-LSPIV can be effectively applied to surface flow studies.

Diffusion weighted MR imaging in the diagnosis of multiple sclerosis

International Nuclear Information System (INIS)

Hagen, T.; Schweigerer-Schroeter, G.; Wellnitz, J.; Wuerstle, T.

2000-01-01

Magnetic resonance (MR) imaging is one of the best methods in diagnosis of multiple sclerosis, particularly in disclosure of active demyelinating lesions. Aim of this study was to compare diffusion weighted imaging and contrast enhancement in the detection of active lesions. A MR study with a contrast enhanced T1-weighted pulse sequence with magnetization transfer presaturation and a diffusion weighted echoplanar pulse sequence (b=1000 s/mm 2 ) was performed in 17 patients (11 women, 6 men) with multiple sclerosis. 29 of 239 lesions showed an increased signal intensity in diffusion weighted imaging, 24 lesions a contrast enhancement, but only 16 lesions were visible in both pulse sequences. In patients with short clinical symptomatology significant more lesions could be detected with diffusion-weighted pulse sequence in comparison to patients with long standing symptomatology showing more lesions with contrast enhancement. Hence it is likely, that both pulse sequences detect different histopathologic changes. The early detection of demyelinating lesions in diffusion weighted imaging is attributed to the extracellular edema, however the contrast enhancement is caused by a blood brain barrier abnormality. It can be expected that diffusion weighted imaging will have a high impact on imaging of multiple sclerosis not only in therapeutic trials, but also in clinical routine. (orig.) [de

A workflow for extracting plot-level biophysical indicators from aerially acquired multispectral imagery

Science.gov (United States)

Advances in technologies associated with unmanned aerial vehicles (UAVs) has allowed for researchers, farmers and agribusinesses to incorporate UAVs coupled with various imaging systems into data collection activities and aid expert systems for making decisions. Multispectral imageries allow for a q...

A lightweight hyperspectral mapping system and photogrammetric processing chain for unmanned aerial vehicles

NARCIS (Netherlands)

Suomalainen, J.M.; Anders, N.S.; Iqbal, S.; Roerink, G.J.; Franke, G.J.; Wenting, P.F.M.; Hünniger, D.; Bartholomeus, H.; Becker, R.; Kooistra, L.

2014-01-01

During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles (UAV). However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg, forcing usage of

Suitable post processing algorithms for X-ray imaging using oversampled displaced multiple images

International Nuclear Information System (INIS)

Thim, J; Reza, S; Nawaz, K; Norlin, B; O'Nils, M; Oelmann, B

2011-01-01

X-ray imaging systems such as photon counting pixel detectors have a limited spatial resolution of the pixels, based on the complexity and processing technology of the readout electronics. For X-ray imaging situations where the features of interest are smaller than the imaging system pixel size, and the pixel size cannot be made smaller in the hardware, alternative means of resolution enhancement require to be considered. Oversampling with the usage of multiple displaced images, where the pixels of all images are mapped to a final resolution enhanced image, has proven a viable method of reaching a sub-pixel resolution exceeding the original resolution. The effectiveness of the oversampling method declines with the number of images taken, the sub-pixel resolution increases, but relative to a real reduction of imaging pixel sizes yielding a full resolution image, the perceived resolution from the sub-pixel oversampled image is lower. This is because the oversampling method introduces blurring noise into the mapped final images, and the blurring relative to full resolution images increases with the oversampling factor. One way of increasing the performance of the oversampling method is by sharpening the images in post processing. This paper focus on characterizing the performance increase of the oversampling method after the use of some suitable post processing filters, for digital X-ray images specifically. The results show that spatial domain filters and frequency domain filters of the same type yield indistinguishable results, which is to be expected. The results also show that the effectiveness of applying sharpening filters to oversampled multiple images increase with the number of images used (oversampling factor), leaving 60-80% of the original blurring noise after filtering a 6 x 6 mapped image (36 images taken), where the percentage is depending on the type of filter. This means that the effectiveness of the oversampling itself increase by using sharpening

High-field MR imaging of spinal cord multiple sclerosis

International Nuclear Information System (INIS)

De La Paz, R.L.; Floris, R.; Norman, D.; Enzmann, D.R.

1987-01-01

Fifty-one high-field MR imaging studies (1.5 T, General Electric Signa) of the spinal cord were performed in 42 patients (27 female, 15 male; mean age, 40 years) with clinically definitive (n = 34) or probable (n = 8) multiple sclerosis and suspected spinal cord lesions. MR imaging showed focal spinal cord abnormalities in 38 (75%) of 51 studies. T2-weighted images were abnormal (showing foci of high signal intensity) in 38 studies, T1-weighted images were abnormal (showing areas of low signal intensity or mass effect) in 16 (42%) of 38, and GRASS images were abnormal (showing foci of high signal intensity) in 9 (82%) of 11 cases. Brain MR imaging showed periventricular lesions typical of multiple sclerosis in 34 (81%) of 42 studies. Spinal cord studies were positive in eight cases with normal brain MR images, and brain studies were positive in 13 instances of normal spinal cord MR images. Four lesions were at the cervicomedullary junction, 44 in the cervical spinal cord, and three in the thoracic cord. Mass effect in cord lesions, simulating neoplasm, was seen in seven patients during the acute symptomatic phase. Serial studies in three patients with decreasing symptoms showed a reduction after 3-4 weeks and resolution of the mass effect after 2-6 months

Land Cover Change Detection in Urban Lake Areas Using Multi-Temporary Very High Spatial Resolution Aerial Images

Directory of Open Access Journals (Sweden)

Wenyuan Zhang

2018-01-01

Full Text Available The availability of very high spatial resolution (VHR remote sensing imagery provides unique opportunities to exploit meaningful change information in detail with object-oriented image analysis. This study investigated land cover (LC changes in Shahu Lake of Wuhan using multi-temporal VHR aerial images in the years 1978, 1981, 1989, 1995, 2003, and 2011. A multi-resolution segmentation algorithm and CART (classification and regression trees classifier were employed to perform highly accurate LC classification of the individual images, while a post-classification comparison method was used to detect changes. The experiments demonstrated that significant changes in LC occurred along with the rapid urbanization during 1978–2011. The dominant changes that took place in the study area were lake and vegetation shrinking, replaced by high density buildings and roads. The total area of Shahu Lake decreased from ~7.64 km2 to ~3.60 km2 during the past 33 years, where 52.91% of its original area was lost. The presented results also indicated that urban expansion and inadequate legislative protection are the main factors in Shahu Lake’s shrinking. The object-oriented change detection schema presented in this manuscript enables us to better understand the specific spatial changes of Shahu Lake, which can be used to make reasonable decisions for lake protection and urban development.

ARM Aerial Facility ArcticShark Unmanned Aerial System

Science.gov (United States)

Schmid, B.; Hubbell, M.; Mei, F.; Carroll, P.; Mendoza, A.; Ireland, C.; Lewko, K.

2017-12-01

The TigerShark Block 3 XP-AR "ArcticShark" Unmanned Aerial System (UAS), developed and manufactured by Navmar Applied Sciences Corporation (NASC), is a single-prop, 60 hp rotary-engine platform with a wingspan of 6.5 m and Maximum Gross Takeoff Weight of 295 Kg. The ArcticShark is owned by the U.S. Department of Energy (DOE) and has been operated by Pacific Northwest National Laboratory (PNNL) since March 2017. The UAS will serve as an airborne atmospheric research observatory for DOE ARM, and, once fully operational, can be requested through ARM's annual call for proposals. The Arctic Shark is anticipated to measure a wide range of radiative, aerosol, and cloud properties using a variable instrument payload weighing up to 46 Kg. SATCOM-equipped, it is capable of taking measurements up to altitudes of 5.5 Km over ranges of up to 500 Km. The ArcticShark operates at airspeeds of 30 to 40 m/s, making it capable of slow sampling. With a full fuel load, its endurance exceeds 8 hours. The aircraft and its Mobile Operations Center (MOC) have been hardened specifically for operations in colder temperatures.ArcticShark's design facilitates rapid integration of various types of payloads. 2500 W of its 4000 W electrical systems is dedicated to payload servicing. It has an interior payload volume of almost 85 L and four wing-mounted pylons capable of carrying external probes. Its payload bay volume, electrical power, payload capacity, and flight characteristics enable the ArcticShark to accommodate multiple combinations of payloads in numerous configurations. Many instruments will be provided by the ARM Aerial Facility (AAF), but other organizations may eventually propose instrumentation for specific campaigns. AAF-provided measurement capabilities will include the following atmospheric state and thermodynamics: temperature, pressure, winds; gases: H2O and CO2; up- and down-welling broadband infrared and visible radiation; surface temperature; aerosol number concentration

Pipeline monitoring with unmanned aerial vehicles

Science.gov (United States)

Kochetkova, L. I.

2018-05-01

Pipeline leakage during transportation of combustible substances leads to explosion and fire thus causing death of people and destruction of production and accommodation facilities. Continuous pipeline monitoring allows identifying leaks in due time and quickly taking measures for their elimination. The paper describes the solution of identification of pipeline leakage using unmanned aerial vehicles. It is recommended to apply the spectral analysis with input RGB signal to identify pipeline damages. The application of multi-zone digital images allows defining potential spill of oil hydrocarbons as well as possible soil pollution. The method of multi-temporal digital images within the visible region makes it possible to define changes in soil morphology for its subsequent analysis. The given solution is cost efficient and reliable thus allowing reducing timing and labor resources in comparison with other methods of pipeline monitoring.

Using unmanned aerial vehicles and structure-from-motion photogrammetry to characterize sedimentary outcrops: An example from the Morrison Formation, Utah, USA

Science.gov (United States)

Chesley, J. T.; Leier, A. L.; White, S.; Torres, R.

2017-06-01

Recently developed data collection techniques allow for improved characterization of sedimentary outcrops. Here, we outline a workflow that utilizes unmanned aerial vehicles (UAV) and structure-from-motion (SfM) photogrammetry to produce sub-meter-scale outcrop reconstructions in 3-D. SfM photogrammetry uses multiple overlapping images and an image-based terrain extraction algorithm to reconstruct the location of individual points from the photographs in 3-D space. The results of this technique can be used to construct point clouds, orthomosaics, and digital surface models that can be imported into GIS and related software for further study. The accuracy of the reconstructed outcrops, with respect to an absolute framework, is improved with geotagged images or independently gathered ground control points, and the internal accuracy of 3-D reconstructions is sufficient for sub-meter scale measurements. We demonstrate this approach with a case study from central Utah, USA, where UAV-SfM data can help delineate complex features within Jurassic fluvial sandstones.

ASSESMENT OF THE INFLUENCE OF UAV IMAGE QUALITY ON THE ORTHOPHOTO PRODUCTION

Directory of Open Access Journals (Sweden)

D. Wierzbicki

2015-08-01

Full Text Available Over the past years a noticeable increase of interest in using Unmanned Aerial Vehicles (UAV for acquiring low altitude images has been observed. This method creates new possibilities of using geodata captured from low altitudes to generate large scale orthophotos. Because of comparatively low costs, UAV aerial surveying systems find many applications in photogrammetry and remote sensing. One of the most significant problems with automation of processing of image data acquired with this method is its low accuracy. This paper presents the following stages of acquisition and processing of images collected in various weather and lighting conditions: aerotriangulation, generating of Digital Terrain Models (DTMs, orthorectification and mosaicking. In the research a compact, non-metric camera, mounted on a fuselage powered by an electric motor was used. The tested area covered flat, agricultural and woodland terrains. Aerotriangulation and point cloud accuracy as well as generated digital terrain model and mosaic exactness were examined. Dense multiple image matching was used as a benchmark. The processing and analysis were carried out with INPHO UASMaster programme. Based on performed accuracy analysis it was stated that images acquired in poor weather conditions (cloudy, precipitation degrade the final quality and accuracy of a photogrammetric product by an average of 25%.

Significance of multiple scattering in imaging through turbid media

International Nuclear Information System (INIS)

Zardecki, A.; Gerstl, S.A.W.

1986-01-01

The degradation of image quality in a turbid medium is analyzed within the framework of the small-angle approximation, the diffusion approximation, and a rigorous two-dimensional radiative transfer equation. These three approaches allow us to emphasize different aspects of the imaging problem when multiple scattering effects are important. For a medium with a forward-peaked phase function, the separation of multiple scattering into a series of scatterings of various order provides a fruitful technique. The use of the diffusion approximation and transport theory extends the determination of the modulation transfer function to a turbid medium with an arbitrary degree of anisotropy

Noninvasive imaging of multiple myeloma using near infrared fluorescent molecular probe

Science.gov (United States)

Hathi, Deep; Zhou, Haiying; Bollerman-Nowlis, Alex; Shokeen, Monica; Akers, Walter J.

2016-03-01

Multiple myeloma is a plasma cell malignancy characterized by monoclonal gammopathy and osteolytic bone lesions. Multiple myeloma is most commonly diagnosed in late disease stages, presenting with pathologic fracture. Early diagnosis and monitoring of disease status may improve quality of life and long-term survival for multiple myeloma patients from what is now a devastating and fatal disease. We have developed a near-infrared targeted fluorescent molecular probe with high affinity to the α4β1 integrin receptor (VLA-4)overexpressed by a majority of multiple myeloma cells as a non-radioactive analog to PET/CT tracer currently being developed for human diagnostics. A near-infrared dye that emits about 700 nm was conjugated to a high affinity peptidomimmetic. Binding affinity and specificity for multiple myeloma cells was investigated in vitro by tissue staining and flow cytometry. After demonstration of sensitivity and specificity, preclinical optical imaging studies were performed to evaluate tumor specificity in murine subcutaneous and metastatic multiple myeloma models. The VLA-4-targeted molecular probe showed high affinity for subcutaneous MM tumor xenografts. Importantly, tumor cells specific accumulation in the bone marrow of metastatic multiple myeloma correlated with GFP signal from transfected cells. Ex vivo flow cytometry of tumor tissue and bone marrow further corroborated in vivo imaging data, demonstrating the specificity of the novel agent and potential for quantitative imaging of multiple myeloma burden in these models.

Analysis of Unmanned Aerial System-Based CIR Images in Forestry—A New Perspective to Monitor Pest Infestation Levels

Directory of Open Access Journals (Sweden)

Jan Rudolf Karl Lehmann

2015-03-01

Full Text Available The detection of pest infestation is an important aspect of forest management. In the case of the oak splendour beetle (Agrilus biguttatus infestation, the affected oaks (Quercus sp. show high levels of defoliation and altered canopy reflection signature. These critical features can be identified in high-resolution colour infrared (CIR images of the tree crown and branches level captured by Unmanned Aerial Systems (UAS. In this study, we used a small UAS equipped with a compact digital camera which has been calibrated and modified to record not only the visual but also the near infrared reflection (NIR of possibly infested oaks. The flight campaigns were realized in August 2013, covering two study sites which are located in a rural area in western Germany. Both locations represent small-scale, privately managed commercial forests in which oaks are economically valuable species. Our workflow includes the CIR/NIR image acquisition, mosaicking, georeferencing and pixel-based image enhancement followed by object-based image classification techniques. A modified Normalized Difference Vegetation Index (NDVImod derived classification was used to distinguish between five vegetation health classes, i.e., infested, healthy or dead branches, other vegetation and canopy gaps. We achieved an overall Kappa Index of Agreement (KIA   of 0.81 and 0.77 for each study site, respectively. This approach offers a low-cost alternative to private forest owners who pursue a sustainable management strategy.

Fusion of LiDAR and aerial imagery for the estimation of downed tree volume using Support Vector Machines classification and region based object fitting

Science.gov (United States)

Selvarajan, Sowmya

The study classifies 3D small footprint full waveform digitized LiDAR fused with aerial imagery to downed trees using Support Vector Machines (SVM) algorithm. Using small footprint waveform LiDAR, airborne LiDAR systems can provide better canopy penetration and very high spatial resolution. The small footprint waveform scanner system Riegl LMS-Q680 is addition with an UltraCamX aerial camera are used to measure and map downed trees in a forest. The various data preprocessing steps helped in the identification of ground points from the dense LiDAR dataset and segment the LiDAR data to help reduce the complexity of the algorithm. The haze filtering process helped to differentiate the spectral signatures of the various classes within the aerial image. Such processes, helped to better select the features from both sensor data. The six features: LiDAR height, LiDAR intensity, LiDAR echo, and three image intensities are utilized. To do so, LiDAR derived, aerial image derived and fused LiDAR-aerial image derived features are used to organize the data for the SVM hypothesis formulation. Several variations of the SVM algorithm with different kernels and soft margin parameter C are experimented. The algorithm is implemented to classify downed trees over a pine trees zone. The LiDAR derived features provided an overall accuracy of 98% of downed trees but with no classification error of 86%. The image derived features provided an overall accuracy of 65% and fusion derived features resulted in an overall accuracy of 88%. The results are observed to be stable and robust. The SVM accuracies were accompanied by high false alarm rates, with the LiDAR classification producing 58.45%, image classification producing 95.74% and finally the fused classification producing 93% false alarm rates The Canny edge correction filter helped control the LiDAR false alarm to 35.99%, image false alarm to 48.56% and fused false alarm to 37.69% The implemented classifiers provided a powerful tool for

Image transmission in multicore-fiber code-division multiple access network

Science.gov (United States)

Yang, Guu-Chang; Kwong, Wing C.

1997-01-01

Recently, two-dimensional (2-D) signature patterns were proposed to encode binary digitized image pixels in optical code-division multiple-access (CDMA) networks with 'multicore' fiber. The new technology enables parallel transmission and simultaneous access of 2-D images in multiple-access environment, where these signature patterns are defined as optical orthogonal signature pattern codes (OOSPCs). However, previous work on OOSPCs assumed that the weight of each signature pattern was the same. In this paper, we construct a new family of OOSPCs with the removal of this assumption. Since varying the weight of a user's signature pattern affects that user's performance, this approach is useful for CDMA optical systems with multiple performance requirements.

COMPARATIVE ASSESSMENT OF VERY HIGH RESOLUTION SATELLITE AND AERIAL ORTHOIMAGERY

Directory of Open Access Journals (Sweden)

P. Agrafiotis

2015-03-01

Full Text Available This paper aims to assess the accuracy and radiometric quality of orthorectified high resolution satellite imagery from Pleiades-1B satellites through a comparative evaluation of their quantitative and qualitative properties. A Pleiades-B1 stereopair of high resolution images taken in 2013, two adjacent GeoEye-1 stereopairs from 2011 and aerial orthomosaic (LSO provided by NCMA S.A (Hellenic Cadastre from 2007 have been used for the comparison tests. As control dataset orthomosaic from aerial imagery provided also by NCMA S.A (0.25m GSD from 2012 was selected. The process for DSM and orthoimage production was performed using commercial digital photogrammetric workstations. The two resulting orthoimages and the aerial orthomosaic (LSO were relatively and absolutely evaluated for their quantitative and qualitative properties. Test measurements were performed using the same check points in order to establish their accuracy both as far as the single point coordinates as well as their distances are concerned. Check points were distributed according to JRC Guidelines for Best Practice and Quality Checking of Ortho Imagery and NSSDA standards while areas with different terrain relief and land cover were also included. The tests performed were based also on JRC and NSSDA accuracy standards. Finally, tests were carried out in order to assess the radiometric quality of the orthoimagery. The results are presented with a statistical analysis and they are evaluated in order to present the merits and demerits of the imaging sensors involved for orthoimage production. The results also serve for a critical approach for the usability and cost efficiency of satellite imagery for the production of Large Scale Orthophotos.

High Dynamic Range Imaging Using Multiple Exposures

Science.gov (United States)

Hou, Xinglin; Luo, Haibo; Zhou, Peipei; Zhou, Wei

2017-06-01

It is challenging to capture a high-dynamic range (HDR) scene using a low-dynamic range (LDR) camera. This paper presents an approach for improving the dynamic range of cameras by using multiple exposure images of same scene taken under different exposure times. First, the camera response function (CRF) is recovered by solving a high-order polynomial in which only the ratios of the exposures are used. Then, the HDR radiance image is reconstructed by weighted summation of the each radiance maps. After that, a novel local tone mapping (TM) operator is proposed for the display of the HDR radiance image. By solving the high-order polynomial, the CRF can be recovered quickly and easily. Taken the local image feature and characteristic of histogram statics into consideration, the proposed TM operator could preserve the local details efficiently. Experimental result demonstrates the effectiveness of our method. By comparison, the method outperforms other methods in terms of imaging quality.

Magnetic resonance imaging abnormalities in multiple sclerosis: A review

International Nuclear Information System (INIS)

Saharian, M. A.; Shakaouri Rad, A.; Motamedi, M.; Pakdaman, H.; Radue, E. W.

2007-01-01

:During the last two decades, magnetic resonance imaging has been widely used In the diagnosis and treatment monitoring of multiple sclerosis. MRI, both conventional and non conventional methods, has transformed all aspects of M S research and clinical practice in recent years. Although advanced imaging methods have added much more to our knowledge about pathogenesis and natural history of the disease but their cost, availability, complexity and lack of validation have limited their use in routine clinical practice. Conventional MR techniques including proton density, T1/T2-Weighted images and fluid- attenuated inversion recovery sequences are now accepted in standard protocols for diagnosis and treatment outcome measures in clinical trials of multiple sclerosis. This review will focus on the type, morphology and evolution of M S lesions regarding conventional MRI and their use for treatment monitoring in daily clinical practice

INTEGRATED FUSION METHOD FOR MULTIPLE TEMPORAL-SPATIAL-SPECTRAL IMAGES

Directory of Open Access Journals (Sweden)

H. Shen

2012-08-01

Full Text Available Data fusion techniques have been widely researched and applied in remote sensing field. In this paper, an integrated fusion method for remotely sensed images is presented. Differently from the existed methods, the proposed method has the performance to integrate the complementary information in multiple temporal-spatial-spectral images. In order to represent and process the images in one unified framework, two general image observation models are firstly presented, and then the maximum a posteriori (MAP framework is used to set up the fusion model. The gradient descent method is employed to solve the fused image. The efficacy of the proposed method is validated using simulated images.

Planning and decision making for aerial robots

CERN Document Server

Bestaoui Sebbane, Yasmina

2014-01-01

This book provides an introduction to the emerging field of planning and decision making for aerial robots. An aerial robot is the ultimate form of Unmanned Aerial Vehicle, an aircraft endowed with built-in intelligence, requiring no direct human control and able to perform a specific task. It must be able to fly within a partially structured environment, to react and adapt to changing environmental conditions and to accommodate for the uncertainty that exists in the physical world. An aerial robot can be termed as a physical agent that exists and flies in the real 3D world, can sense its environment and act on it to achieve specific goals. So throughout this book, an aerial robot will also be termed as an agent.   Fundamental problems in aerial robotics include the tasks of spatial motion, spatial sensing and spatial reasoning. Reasoning in complex environments represents a difficult problem. The issues specific to spatial reasoning are planning and decision making. Planning deals with the trajectory algori...

Person identification from aerial footage by a remote-controlled drone.

Science.gov (United States)

Bindemann, Markus; Fysh, Matthew C; Sage, Sophie S K; Douglas, Kristina; Tummon, Hannah M

2017-10-19

Remote-controlled aerial drones (or unmanned aerial vehicles; UAVs) are employed for surveillance by the military and police, which suggests that drone-captured footage might provide sufficient information for person identification. This study demonstrates that person identification from drone-captured images is poor when targets are unfamiliar (Experiment 1), when targets are familiar and the number of possible identities is restricted by context (Experiment 2), and when moving footage is employed (Experiment 3). Person information such as sex, race and age is also difficult to access from drone-captured footage (Experiment 4). These findings suggest that such footage provides a particularly poor medium for person identification. This is likely to reflect the sub-optimal quality of such footage, which is subject to factors such as the height and velocity at which drones fly, viewing distance, unfavourable vantage points, and ambient conditions.

Earth mapping - aerial or satellite imagery comparative analysis

Science.gov (United States)

Fotev, Svetlin; Jordanov, Dimitar; Lukarski, Hristo

Nowadays, solving the tasks for revision of existing map products and creation of new maps requires making a choice of the land cover image source. The issue of the effectiveness and cost of the usage of aerial mapping systems versus the efficiency and cost of very-high resolution satellite imagery is topical [1, 2, 3, 4]. The price of any remotely sensed image depends on the product (panchromatic or multispectral), resolution, processing level, scale, urgency of task and on whether the needed image is available in the archive or has to be requested. The purpose of the present work is: to make a comparative analysis between the two approaches for mapping the Earth having in mind two parameters: quality and cost. To suggest an approach for selection of the map information sources - airplane-based or spacecraft-based imaging systems with very-high spatial resolution. Two cases are considered: area that equals approximately one satellite scene and area that equals approximately the territory of Bulgaria.

Simultaneous reconstruction of multiple depth images without off-focus points in integral imaging using a graphics processing unit.

Science.gov (United States)

Yi, Faliu; Lee, Jieun; Moon, Inkyu

2014-05-01

The reconstruction of multiple depth images with a ray back-propagation algorithm in three-dimensional (3D) computational integral imaging is computationally burdensome. Further, a reconstructed depth image consists of a focus and an off-focus area. Focus areas are 3D points on the surface of an object that are located at the reconstructed depth, while off-focus areas include 3D points in free-space that do not belong to any object surface in 3D space. Generally, without being removed, the presence of an off-focus area would adversely affect the high-level analysis of a 3D object, including its classification, recognition, and tracking. Here, we use a graphics processing unit (GPU) that supports parallel processing with multiple processors to simultaneously reconstruct multiple depth images using a lookup table containing the shifted values along the x and y directions for each elemental image in a given depth range. Moreover, each 3D point on a depth image can be measured by analyzing its statistical variance with its corresponding samples, which are captured by the two-dimensional (2D) elemental images. These statistical variances can be used to classify depth image pixels as either focus or off-focus points. At this stage, the measurement of focus and off-focus points in multiple depth images is also implemented in parallel on a GPU. Our proposed method is conducted based on the assumption that there is no occlusion of the 3D object during the capture stage of the integral imaging process. Experimental results have demonstrated that this method is capable of removing off-focus points in the reconstructed depth image. The results also showed that using a GPU to remove the off-focus points could greatly improve the overall computational speed compared with using a CPU.

UCXp camera imaging principle and key technologies of data post-processing

International Nuclear Information System (INIS)

Yuan, Fangyan; Li, Guoqing; Zuo, Zhengli; Liu, Jianmin; Wu, Liang; Yu, Xiaoping; Zhao, Haitao

2014-01-01

The large format digital aerial camera product UCXp was introduced into the Chinese market in 2008, the image consists of 17310 columns and 11310 rows with a pixel size of 6 mm. The UCXp camera has many advantages compared with the same generation camera, with multiple lenses exposed almost at the same time and no oblique lens. The camera has a complex imaging process whose principle will be detailed in this paper. On the other hand, the UCXp image post-processing method, including data pre-processing and orthophoto production, will be emphasized in this article. Based on the data of new Beichuan County, this paper will describe the data processing and effects

UCXp camera imaging principle and key technologies of data post-processing

Science.gov (United States)

Yuan, Fangyan; Li, Guoqing; Zuo, Zhengli; Liu, Jianmin; Wu, Liang; Yu, Xiaoping; Zhao, Haitao

2014-03-01

The large format digital aerial camera product UCXp was introduced into the Chinese market in 2008, the image consists of 17310 columns and 11310 rows with a pixel size of 6 mm. The UCXp camera has many advantages compared with the same generation camera, with multiple lenses exposed almost at the same time and no oblique lens. The camera has a complex imaging process whose principle will be detailed in this paper. On the other hand, the UCXp image post-processing method, including data pre-processing and orthophoto production, will be emphasized in this article. Based on the data of new Beichuan County, this paper will describe the data processing and effects.

Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs

Directory of Open Access Journals (Sweden)

Friederike Gnädinger

2017-05-01

Full Text Available Precision phenotyping, especially the use of image analysis, allows researchers to gain information on plant properties and plant health. Aerial image detection with unmanned aerial vehicles (UAVs provides new opportunities in precision farming and precision phenotyping. Precision farming has created a critical need for spatial data on plant density. The plant number reflects not only the final field emergence but also allows a more precise assessment of the final yield parameters. The aim of this work is to advance UAV use and image analysis as a possible high-throughput phenotyping technique. In this study, four different maize cultivars were planted in plots with different seeding systems (in rows and equidistantly spaced and different nitrogen fertilization levels (applied at 50, 150 and 250 kg N/ha. The experimental field, encompassing 96 plots, was overflown at a 50-m height with an octocopter equipped with a 10-megapixel camera taking a picture every 5 s. Images were recorded between BBCH 13–15 (it is a scale to identify the phenological development stage of a plant which is here the 3- to 5-leaves development stage when the color of young leaves differs from older leaves. Close correlations up to R2 = 0.89 were found between in situ and image-based counted plants adapting a decorrelation stretch contrast enhancement procedure, which enhanced color differences in the images. On average, the error between visually and digitally counted plants was ≤5%. Ground cover, as determined by analyzing green pixels, ranged between 76% and 83% at these stages. However, the correlation between ground cover and digitally counted plants was very low. The presence of weeds and blurry effects on the images represent possible errors in counting plants. In conclusion, the final field emergence of maize can rapidly be assessed and allows more precise assessment of the final yield parameters. The use of UAVs and image processing has the potential to

Historical Image Registration and Land-Use Land-Cover Change Analysis

Directory of Open Access Journals (Sweden)

Fang-Ju Jao

2014-12-01

Full Text Available Historical aerial images are important to retain past ground surface information. The land-use land-cover change in the past can be identified using historical aerial images. Automatic historical image registration and stitching is essential because the historical image pose information was usually lost. In this study, the Scale Invariant Feature Transform (SIFT algorithm was used for feature extraction. Subsequently, the present study used the automatic affine transformation algorithm for historical image registration, based on SIFT features and control points. This study automatically determined image affine parameters and simultaneously transformed from an image coordinate system to a ground coordinate system. After historical aerial image registration, the land-use land-cover change was analyzed between two different years (1947 and 1975 at the Tseng Wen River estuary. Results show that sandbars and water zones were transformed into a large number of fish ponds between 1947 and 1975.

Multiple-Targeted Graphene-based Nanocarrier for Intracellular Imaging of mRNAs

Energy Technology Data Exchange (ETDEWEB)

Wang, Ying; Li, Zhaohui; Liu, Misha; Hu, Dehong; Lin, Yuehe; Li, Jinghong

2017-08-29

Simultaneous detection and imaging of multiple intracellular messenger RNA (mRNAs) hold great significant for early cancer diagnostics and preventive medicine development. Herein, we propose a multiple-targeted graphene oxide (GO) nanocarrier that can simultaneously detect and image different type mRNAs in living cells. First of all, in vitro detection of multiple targets have been realized successfully based on the multiple-targeted GO nanocarrier with linear relationship ranging from 3 nM to 200 nM, as well as sensitive detection limit of 1.84 nM for manganese superoxide dismutase (Mn-SOD) mRNA and 2.45 nM for β-actin mRNA. Additionally, this nanosensing platform composed of fluorescent labeled single strand DNA probes and GO nanocarrier can identify Mn-SOD mRNA and endogenous mRNA of β-actin in living cancer cells, showing rapid response, high specificity, nuclease stability, and good biocompatibility during the cell imaging. Thirdly, changes of the expression levels of mRNA in living cells before or after the drug treatment can be monitored successfully. By using multiple ssDNA as probes and GO nanocarrier as the cellular delivery cargo, the proposed simultaneous multiple-targeted sensing platform will be of great potential as a powerful tool for intracellular trafficking process from basic research to clinical diagnosis.

Quantification of marine macro-debris abundance around Vancouver Island, Canada, based on archived aerial photographs processed by projective transformation.

Science.gov (United States)

Kataoka, Tomoya; Murray, Cathryn Clarke; Isobe, Atsuhiko

2017-09-12

The abundance of marine macro-debris was quantified with high spatial resolution by applying an image processing technique to archived shoreline aerial photographs taken over Vancouver Island, Canada. The photographs taken from an airplane at oblique angles were processed by projective transformation for georeferencing, where five reference points were defined by comparing aerial photographs with satellite images of Google Earth. Thereafter, pixels of marine debris were extracted based on their color differences from the background beaches. The debris abundance can be evaluated by the ratio of an area covered by marine debris to that of the beach (percent cover). The horizontal distribution of percent cover of marine debris was successfully computed from 167 aerial photographs and was significantly related to offshore Ekman flows and winds (leeway drift and Stokes drift). Therefore, the estimated percent cover is useful information to determine priority sites for mitigating adverse impacts across broad areas. Copyright © 2017 Elsevier Ltd. All rights reserved.

1988 Mosaic of Aerial Photography of the Salt River Bay National Historical Park and Ecological Preserve

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Aerial photographs taken by NOAA's National Geodetic Survey during 1988 were mosaicked and orthorectified by the Biogeography Branch. The resulting image was used to...

1992 Mosaic of Aerial Photography of the Salt River Bay National Historical Park and Ecological Preserve

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Aerial photographs taken by NOAA's National Geodetic Survey during 1992 were mosaicked and orthorectified by the Biogeography Branch. The resulting image was used to...

2000 Mosaic of Aerial Photography of the Salt River Bay National Historical Park and Ecological Preserve

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Aerial photographs taken by NOAA's National Geodetic Survey during 2000 were mosaicked and orthorectified by the Biogeography Branch. The resulting image was used to...

Intra- and interspecific variation in tropical tree and liana phenology derived from Unmanned Aerial Vehicle images

Science.gov (United States)

Bohlman, S.; Park, J.; Muller-Landau, H. C.; Rifai, S. W.; Dandois, J. P.

2017-12-01

Phenology is a critical driver of ecosystem processes. There is strong evidence that phenology is shifting in temperate ecosystems in response to climate change, but tropical tree and liana phenology remains poorly quantified and understood. A key challenge is that tropical forests contain hundreds of plant species with a wide variety of phenological patterns. Satellite-based observations, an important source of phenology data in northern latitudes, are hindered by frequent cloud cover in the tropics. To quantify phenology over a large number of individuals and species, we collected bi-weekly images from unmanned aerial vehicles (UAVs) in the well-studied 50-ha forest inventory plot on Barro Colorado Island, Panama. Between October 2014 and December 2015 and again in May 2015, we collected a total of 35 sets of UAV images, each with continuous coverage of the 50-ha plot, where every tree ≥ 1 cm DBH is mapped. Spectral, texture, and image information was extracted from the UAV images for individual tree crowns, which was then used as inputs for a machine learning algorithm to predict percent leaf and branch cover. We obtained the species identities of 2000 crowns in the images via field mapping. The objectives of this study are to (1) determined if machine learning algorithms, applied to UAV images, can effectively quantify changes in leaf cover, which we term "deciduousness; (2) determine how liana cover effects deciduousness and (3) test how well UAV-derived deciduousness patterns match satellite-derived temporal patterns. Machine learning algorithms trained on a variety of image parameters could effectively determine leaf cover, despite variation in lighting and viewing angles. Crowns with higher liana cover have less overall deciduousness (tree + liana together) than crowns with lower liana cover. Individual crown deciduousness, summed over all crowns measured in the 50-ha plot, showed a similar seasonal pattern as MODIS EVI composited over 10 years. However

MR imaging of multiple sclerosis in the cervical cord

International Nuclear Information System (INIS)

Shakudo, Miyuki; Takemoto, Kazumasa; Inoue, Yuichi; Onoyama, Yasuto; Nishimura, Masataka; Fujita, Masayuki.

1987-01-01

This is a case of a 34-year-old woman with multiple sclerosis (MS) in whom an enlarged cervical spinal cord with long T 1 and T 2 relaxation times was demonstrated on MR images. This report seems to be the first description of MR imaging of MS with an enlarged spinal cord. (author)

Magnetic resonance imaging in the diagnostics of multiple sclerosis

International Nuclear Information System (INIS)

Larsen, J.P.; Tjoerstad, K.; Kaass, B.; Oedegaard, H.

1987-01-01

Multiple sclerosis is an important and frequent neurological disease and the diagnosis might be difficult. The clinical criteria of multiple sclerosis and the role of laboratory examinations in the diagnosis of the disease are discussed. In particular the help offered by the magnetic resonance imaging method is the subject of this paper. Three patients are reported and discussed

Pseudo natural colour aerial imagery for urban and suburban mapping

DEFF Research Database (Denmark)

Knudsen, Thomas

2005-01-01

Due to their near-infrared data channel, digital airborne four-channel imagers provide a potentially good discrimination between vegetation and human-made materials, which is very useful in automated mapping. Due to their red, green and blue data channels, they also provide natural colour images......, which are very useful in traditional (manual) mapping. In this paper, an algorithm is described which provides an approximation to the spectral capabilities of the four-channel imagers by using a colour-infrared aerial photo as input. The algorithm is tailored to urban/suburban surroundings, where...... the quality of the generated (pseudo) natural colour images are fully acceptable for manual mapping. This brings the combined availability of near-infrared and (pseudo) natural colours within reach for mapping projects based on traditional photogrammetry, which is valuable since traditional analytical cameras...

Satellite Images and Aerial Photographs of the Effects of Hurricanes Katrina and Rita on Coastal Louisiana

Science.gov (United States)

Barras, John A.

2007-01-01

-water datasets derived from the Landsat TM satellite imagery were combined with 2001 marsh vegetative communities (Chabreck and others, unpub. data, 2001) to identify land-water configurations by marsh community before and after the hurricanes. Links to the Landsat TM images and aerial photographs are given below (figs. 1-29). Comparison of land area before the storms to land area after the storms is made possible by the inclusion of Landsat TM images and aerial photographs taken in the years and months before the storms. The figures are arranged geographically from east to west to follow the chronology of the effects of the storms. For a more detailed analysis of the changes wrought by these storms, see 'Land Area Changes in Coastal Louisiana After Hurricanes Katrina and Rita' (Barras, in press).

Monitoring Seabirds and Marine Mammals by Georeferenced Aerial Photography

Science.gov (United States)

Kemper, G.; Weidauer, A.; Coppack, T.

2016-06-01

The assessment of anthropogenic impacts on the marine environment is challenged by the accessibility, accuracy and validity of biogeographical information. Offshore wind farm projects require large-scale ecological surveys before, during and after construction, in order to assess potential effects on the distribution and abundance of protected species. The robustness of site-specific population estimates depends largely on the extent and design of spatial coverage and the accuracy of the applied census technique. Standard environmental assessment studies in Germany have so far included aerial visual surveys to evaluate potential impacts of offshore wind farms on seabirds and marine mammals. However, low flight altitudes, necessary for the visual classification of species, disturb sensitive bird species and also hold significant safety risks for the observers. Thus, aerial surveys based on high-resolution digital imagery, which can be carried out at higher (safer) flight altitudes (beyond the rotor-swept zone of the wind turbines) have become a mandatory requirement, technically solving the problem of distant-related observation bias. A purpose-assembled imagery system including medium-format cameras in conjunction with a dedicated geo-positioning platform delivers series of orthogonal digital images that meet the current technical requirements of authorities for surveying marine wildlife at a comparatively low cost. At a flight altitude of 425 m, a focal length of 110 mm, implemented forward motion compensation (FMC) and exposure times ranging between 1/1600 and 1/1000 s, the twin-camera system generates high quality 16 bit RGB images with a ground sampling distance (GSD) of 2 cm and an image footprint of 155 x 410 m. The image files are readily transferrable to a GIS environment for further editing, taking overlapping image areas and areas affected by glare into account. The imagery can be routinely screened by the human eye guided by purpose-programmed software

MONITORING SEABIRDS AND MARINE MAMMALS BY GEOREFERENCED AERIAL PHOTOGRAPHY

Directory of Open Access Journals (Sweden)

G. Kemper

2016-06-01

Full Text Available The assessment of anthropogenic impacts on the marine environment is challenged by the accessibility, accuracy and validity of biogeographical information. Offshore wind farm projects require large-scale ecological surveys before, during and after construction, in order to assess potential effects on the distribution and abundance of protected species. The robustness of site-specific population estimates depends largely on the extent and design of spatial coverage and the accuracy of the applied census technique. Standard environmental assessment studies in Germany have so far included aerial visual surveys to evaluate potential impacts of offshore wind farms on seabirds and marine mammals. However, low flight altitudes, necessary for the visual classification of species, disturb sensitive bird species and also hold significant safety risks for the observers. Thus, aerial surveys based on high-resolution digital imagery, which can be carried out at higher (safer flight altitudes (beyond the rotor-swept zone of the wind turbines have become a mandatory requirement, technically solving the problem of distant-related observation bias. A purpose-assembled imagery system including medium-format cameras in conjunction with a dedicated geo-positioning platform delivers series of orthogonal digital images that meet the current technical requirements of authorities for surveying marine wildlife at a comparatively low cost. At a flight altitude of 425 m, a focal length of 110 mm, implemented forward motion compensation (FMC and exposure times ranging between 1/1600 and 1/1000 s, the twin-camera system generates high quality 16 bit RGB images with a ground sampling distance (GSD of 2 cm and an image footprint of 155 x 410 m. The image files are readily transferrable to a GIS environment for further editing, taking overlapping image areas and areas affected by glare into account. The imagery can be routinely screened by the human eye guided by

AMS/NRCan Joint Survey Report: Aerial Campaign

Energy Technology Data Exchange (ETDEWEB)

Wasiolek, Piotr [National Security Technologies, LLC. (NSTec), Mercury, NV (United States); Stampahar, Jez [National Security Technologies, LLC. (NSTec), Mercury, NV (United States); Malchow, Rusty [National Security Technologies, LLC. (NSTec), Mercury, NV (United States); Stampahar, Tom [National Security Technologies, LLC. (NSTec), Mercury, NV (United States); Lukens, Mike [National Security Technologies, LLC. (NSTec), Mercury, NV (United States); Seywerd, Henry [Natural Resources Canada (Canada); Fortin, Richard [Natural Resources Canada (Canada); Harvey, Brad [Natural Resources Canada (Canada); Sinclair, Laurel [Natural Resources Canada (Canada)

2014-12-31

In January 2014 the U.S. Department of Energy (DOE), National Nuclear Security Administration (NNSA) Aerial Measuring System (AMS) and the Natural Resources Canada (NRCan) Nuclear Emergency Response project conducted a series of joint surveys at a number of locations in Nevada including the Nevada National Security Site (NNSS). The goal of this project was to compare the responses of the two agencies’ aerial radiation detection systems and data analysis techniques. This test included varied radioactive surface contamination levels and isotopic composition experienced at the NNSS and the differing data processing techniques utilized by the respective teams. Because both teams used the commercial aerial radiation detection systems from Radiation Solutions, Inc., the main focus of the campaign was to investigate the data acquisition techniques, data analysis, and ground-truth verification. The NRCan system consisted of four 4" × 4" × 16" NaI(Tl) scintillator crystals of which two were externally mounted in a modified commercial cargo basket certified for the Eurocopter AS350; the NNSA AMS system consisted of twelve 2" × 4" × 16" NaI(Tl) crystals in externally mounted dedicated pods. For NRCan, the joint survey provided an opportunity to characterize their system’s response to extended sources of various fission products at the NNSS. Since both systems play an important role in their respective countries’ national framework of radiological emergency response and are subject to multiple mutual cooperation agreements, it was important for each country to obtain more thorough knowledge of how they would employ these important assets and define the roles that they would each play in an actual response.

The sky is the limit: reconstructing physical geography fieldwork from an aerial perspective

Science.gov (United States)

Williams, R.; Tooth, S.; Gibson, M.; Barrett, B.

2017-12-01

In an era of rapid geographical data acquisition, interpretations of remote sensing products (e.g. aerial photographs, satellite images, digital elevation models) are an integral part of many undergraduate geography degree schemes but there are fewer opportunities for collection and processing of primary remote sensing data. Unmanned aerial vehicles (UAVs) provide a relatively cheap opportunity to introduce the principles and practice of airborne remote sensing into fieldcourses, enabling students to learn about image acquisition, data processing and interpretation of derived products. Three case studies illustrate how a low cost DJI Phantom UAV can be used by students to acquire images that can be processed using off the shelf Structure-from-Motion photogrammetry software. Two case studies are drawn from an international fieldcourse that takes students to field sites that are the focus of current funded research whilst a third case study is from a course in topographic mapping. Results from a student questionnaire and analysis of assessed student reports showed that using UAVs in fieldwork enhanced student engagement with themes on their fieldcourse and equipped them with data processing skills. The derivation of bespoke orthophotos and Digital Elevation Models also provided students with opportunities to gain insight into the various data quality issues that are associated with aerial imagery acquisition and topographic reconstruction, although additional training is required to maximise this potential. Recognition of the successes and limitations of this teaching intervention provides scope for improving exercises that use UAVs and other technologies in future fieldcourses. UAVs are enabling both a reconstruction of how we measure the Earth's surface and a reconstruction of how students do fieldwork.

Accuracy assessment of topographic mapping using UAV image integrated with satellite images

International Nuclear Information System (INIS)

Azmi, S M; Ahmad, Baharin; Ahmad, Anuar

2014-01-01

Unmanned Aerial Vehicle or UAV is extensively applied in various fields such as military applications, archaeology, agriculture and scientific research. This study focuses on topographic mapping and map updating. UAV is one of the alternative ways to ease the process of acquiring data with lower operating costs, low manufacturing and operational costs, plus it is easy to operate. Furthermore, UAV images will be integrated with QuickBird images that are used as base maps. The objective of this study is to make accuracy assessment and comparison between topographic mapping using UAV images integrated with aerial photograph and satellite image. The main purpose of using UAV image is as a replacement for cloud covered area which normally exists in aerial photograph and satellite image, and for updating topographic map. Meanwhile, spatial resolution, pixel size, scale, geometric accuracy and correction, image quality and information contents are important requirements needed for the generation of topographic map using these kinds of data. In this study, ground control points (GCPs) and check points (CPs) were established using real time kinematic Global Positioning System (RTK-GPS) technique. There are two types of analysis that are carried out in this study which are quantitative and qualitative assessments. Quantitative assessment is carried out by calculating root mean square error (RMSE). The outputs of this study include topographic map and orthophoto. From this study, the accuracy of UAV image is ± 0.460 m. As conclusion, UAV image has the potential to be used for updating of topographic maps

Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery

Directory of Open Access Journals (Sweden)

Yalong Ma

2016-03-01

Full Text Available Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs, more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG and Discrete Cosine Transform (DCT features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness.

Software for roof defects recognition on aerial photographs

Science.gov (United States)

Yudin, D.; Naumov, A.; Dolzhenko, A.; Patrakova, E.

2018-05-01

The article presents information on software for roof defects recognition on aerial photographs, made with air drones. An areal image segmentation mechanism is described. It allows detecting roof defects – unsmoothness that causes water stagnation after rain. It is shown that HSV-transformation approach allows quick detection of stagnation areas, their size and perimeters, but is sensitive to shadows and changes of the roofing-types. Deep Fully Convolutional Network software solution eliminates this drawback. The tested data set consists of the roofing photos with defects and binary masks for them. FCN approach gave acceptable results of image segmentation in Dice metric average value. This software can be used in inspection automation of roof conditions in the production sector and housing and utilities infrastructure.

Tracking stormwater discharge plumes and water quality of the Tijuana River with multispectral aerial imagery

Science.gov (United States)

Svejkovsky, Jan; Nezlin, Nikolay P.; Mustain, Neomi M.; Kum, Jamie B.

2010-04-01

Spatial-temporal characteristics and environmental factors regulating the behavior of stormwater runoff from the Tijuana River in southern California were analyzed utilizing very high resolution aerial imagery, and time-coincident environmental and bacterial sampling data. Thirty nine multispectral aerial images with 2.1-m spatial resolution were collected after major rainstorms during 2003-2008. Utilizing differences in color reflectance characteristics, the ocean surface was classified into non-plume waters and three components of the runoff plume reflecting differences in age and suspended sediment concentrations. Tijuana River discharge rate was the primary factor regulating the size of the freshest plume component and its shorelong extensions to the north and south. Wave direction was found to affect the shorelong distribution of the shoreline-connected fresh plume components much more strongly than wind direction. Wave-driven sediment resuspension also significantly contributed to the size of the oldest plume component. Surf zone bacterial samples collected near the time of each image acquisition were used to evaluate the contamination characteristics of each plume component. The bacterial contamination of the freshest plume waters was very high (100% of surf zone samples exceeded California standards), but the oldest plume areas were heterogeneous, including both polluted and clean waters. The aerial imagery archive allowed study of river runoff characteristics on a plume component level, not previously done with coarser satellite images. Our findings suggest that high resolution imaging can quickly identify the spatial extents of the most polluted runoff but cannot be relied upon to always identify the entire polluted area. Our results also indicate that wave-driven transport is important in distributing the most contaminated plume areas along the shoreline.

Reconstructing building mass models from UAV images

KAUST Repository

Li, Minglei; Nan, Liangliang; Smith, Neil; Wonka, Peter

2015-01-01

We present an automatic reconstruction pipeline for large scale urban scenes from aerial images captured by a camera mounted on an unmanned aerial vehicle. Using state-of-the-art Structure from Motion and Multi-View Stereo algorithms, we first

Aerial shaking performance of wet Anna's hummingbirds

Science.gov (United States)

Ortega-Jimenez, Victor Manuel; Dudley, Robert

2012-01-01

External wetting poses problems of immediate heat loss and long-term pathogen growth for vertebrates. Beyond these risks, the locomotor ability of smaller animals, and particularly of fliers, may be impaired by water adhering to the body. Here, we report on the remarkable ability of hummingbirds to perform rapid shakes in order to expel water from their plumage even while in flight. Kinematic performance of aerial versus non-aerial shakes (i.e. those performed while perching) was compared. Oscillation frequencies of the head, body and tail were lower in aerial shakes. Tangential speeds and accelerations of the trunk and tail were roughly similar in aerial and non-aerial shakes, but values for head motions in air were twice as high when compared with shakes while perching. Azimuthal angular amplitudes for both aerial and non-aerial shakes reached values greater than 180° for the head, greater than 45° for the body trunk and slightly greater than 90° for the tail and wings. Using a feather on an oscillating disc to mimic shaking motions, we found that bending increased average speeds by up to 36 per cent and accelerations of the feather tip up to fourfold relative to a hypothetical rigid feather. Feather flexibility may help to enhance shedding of water and reduce body oscillations during shaking. PMID:22072447

1944 AAF 661 Aerial Photo Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

1943 AAF 332 Aerial Photo Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

1944 AAF 547 Aerial Photo Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

1944 AAF 649 Aerial Photo Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

The Photo-Mosaic Assistant: Incorporating Historic Aerial Imagery into Modern Research Projects

Science.gov (United States)

Flathers, E.

2013-12-01

One challenge that researchers face as data organization and analysis shift into the digital realm is the incorporation of 'dirty' data from analog back-catalogs into current projects. Geospatial data collections in university libraries, government data repositories, and private industry contain historic data such as aerial photographs that may be stored as negatives, prints, and as scanned digital image files. A typical aerial imagery series is created by taking photos of the ground from an aircraft along a series of parallel flight lines. The raw photos can be assembled into a mosaic that represents the full geographic area of the collection, but each photo suffers from individual distortion according to the attitude and altitude of the collecting aircraft at the moment of acquisition, so there is a process of orthorectification needed in order to produce a planimetric composite image that can be used to accurately refer to locations on the ground. Historic aerial photo collections often need significant preparation for consumption by a GIS: they may need to be digitized, often lack any explicit spatial coordinates, and may not include information about flight line patterns. Many collections lack even such basic information as index numbers for the photos, so it may be unclear in what order the photos were acquired. When collections contain large areas of, for example, forest or agricultural land, any given photo may have few visual cues to assist in relating it to the other photos or to an area on the ground. The Photo-Mosaic Assistant (PMA) is a collection of tools designed to assist in the organization of historic aerial photo collections and the preparation of collections for orthorectification and use in modern research applications. The first tool is a light table application that allows a user to take advantage of visual cues within photos to organize and explore the collection, potentially building a rough image mosaic by hand. The second tool is a set of

Woodland Mapping at Single-Tree Levels Using Object-Oriented Classification of Unmanned Aerial Vehicle (uav) Images

Science.gov (United States)

Chenari, A.; Erfanifard, Y.; Dehghani, M.; Pourghasemi, H. R.

2017-09-01

Remotely sensed datasets offer a reliable means to precisely estimate biophysical characteristics of individual species sparsely distributed in open woodlands. Moreover, object-oriented classification has exhibited significant advantages over different classification methods for delineation of tree crowns and recognition of species in various types of ecosystems. However, it still is unclear if this widely-used classification method can have its advantages on unmanned aerial vehicle (UAV) digital images for mapping vegetation cover at single-tree levels. In this study, UAV orthoimagery was classified using object-oriented classification method for mapping a part of wild pistachio nature reserve in Zagros open woodlands, Fars Province, Iran. This research focused on recognizing two main species of the study area (i.e., wild pistachio and wild almond) and estimating their mean crown area. The orthoimage of study area was consisted of 1,076 images with spatial resolution of 3.47 cm which was georeferenced using 12 ground control points (RMSE=8 cm) gathered by real-time kinematic (RTK) method. The results showed that the UAV orthoimagery classified by object-oriented method efficiently estimated mean crown area of wild pistachios (52.09±24.67 m2) and wild almonds (3.97±1.69 m2) with no significant difference with their observed values (α=0.05). In addition, the results showed that wild pistachios (accuracy of 0.90 and precision of 0.92) and wild almonds (accuracy of 0.90 and precision of 0.89) were well recognized by image segmentation. In general, we concluded that UAV orthoimagery can efficiently produce precise biophysical data of vegetation stands at single-tree levels, which therefore is suitable for assessment and monitoring open woodlands.

Optical image encryption scheme with multiple light paths based on compressive ghost imaging

Science.gov (United States)

Zhu, Jinan; Yang, Xiulun; Meng, Xiangfeng; Wang, Yurong; Yin, Yongkai; Sun, Xiaowen; Dong, Guoyan

2018-02-01

An optical image encryption method with multiple light paths is proposed based on compressive ghost imaging. In the encryption process, M random phase-only masks (POMs) are generated by means of logistic map algorithm, and these masks are then uploaded to the spatial light modulator (SLM). The collimated laser light is divided into several beams by beam splitters as it passes through the SLM, and the light beams illuminate the secret images, which are converted into sparse images by discrete wavelet transform beforehand. Thus, the secret images are simultaneously encrypted into intensity vectors by ghost imaging. The distances between the SLM and secret images vary and can be used as the main keys with original POM and the logistic map algorithm coefficient in the decryption process. In the proposed method, the storage space can be significantly decreased and the security of the system can be improved. The feasibility, security and robustness of the method are further analysed through computer simulations.

Use of thermocameras in volcanic areas: mapping of soil radiation; ground and aerial recordings and propositions for space investigation

Energy Technology Data Exchange (ETDEWEB)

Lechi, G M; Marino, C M

1972-03-01

Thermal cameras are capable of providing panoramic, synthetic photographs of thermal structures, rapid and precise quantitative evaluations, and short term or long term observation periods. Theories of electromagnetic radiation are briefly reviewed and possible errors of measurement are discussed. Calibration using a black body as a reference is described. The thermal images obtained may be integrated with aerial photography and/or transcribed in colors which correspond to given levels of radiation. Data acquired in ground surveys may be combined with those derived aerially. The choice of a fixed color reference permits monitoring of temporal changes. Scanning images of the Solfatara di Pozznoli are provided, as are the thermal maps prepared from them.

Importance of multidetector CT imaging in multiple trauma

International Nuclear Information System (INIS)

Linsenmaier, U.; Geyer, L.L.; Reiser, M.; Wirth, S.; Koerner, M.

2014-01-01

Diagnostic imaging of complex multiple trauma remains a challenge for any department providing modern emergency radiology (ER) service. An early and comprehensive approach for ER imaging is crucial for a priority-oriented and timely therapy concept with the aim of identifying potentially life-threatening injuries early and initiating appropriate treatment. The basic diagnostic approach still consists of focused ultrasound using focused assessment with sonography for trauma (FAST) and conventional radiography (CR), usually limited to a single supine chest x-ray for triaging patients undergoing immediate operations. Multidetector computed tomography (MDCT) has become established as early whole body CT (WBCT) as the undisputable diagnostic method. The detection rate of injuries by WBCT is outstanding and it improves the probability of survival by 20-25 % compared with all other previous methods. At the same time, the spatial and temporal resolution of MDCT was improved resulting in considerably shortened examination times but WBCT is still associated with a significant radiation exposure, even in the acute single use setting. Using modern scanner and dose reduction technology, including iterative reconstruction, a dose reduction of up to 40 % could be achieved. The substantial number of images in WBCT is another challenge; images must be processed priority-oriented, read and transferred to the picture archiving and communications system (PACS). For rapid diagnosis, volume image reading (VIR) offers additional options to keep the diagnostic process on time. Modern WBCT after multiple trauma is performed early, comprehensively and personalized so that WBCT improves the probability of survival by 20-25 %. (orig.) [de

Multiple-Event, Single-Photon Counting Imaging Sensor

Science.gov (United States)

Zheng, Xinyu; Cunningham, Thomas J.; Sun, Chao; Wang, Kang L.

2011-01-01

The single-photon counting imaging sensor is typically an array of silicon Geiger-mode avalanche photodiodes that are monolithically integrated with CMOS (complementary metal oxide semiconductor) readout, signal processing, and addressing circuits located in each pixel and the peripheral area of the chip. The major problem is its single-event method for photon count number registration. A single-event single-photon counting imaging array only allows registration of up to one photon count in each of its pixels during a frame time, i.e., the interval between two successive pixel reset operations. Since the frame time can t be too short, this will lead to very low dynamic range and make the sensor merely useful for very low flux environments. The second problem of the prior technique is a limited fill factor resulting from consumption of chip area by the monolithically integrated CMOS readout in pixels. The resulting low photon collection efficiency will substantially ruin any benefit gained from the very sensitive single-photon counting detection. The single-photon counting imaging sensor developed in this work has a novel multiple-event architecture, which allows each of its pixels to register as more than one million (or more) photon-counting events during a frame time. Because of a consequently boosted dynamic range, the imaging array of the invention is capable of performing single-photon counting under ultra-low light through high-flux environments. On the other hand, since the multiple-event architecture is implemented in a hybrid structure, back-illumination and close-to-unity fill factor can be realized, and maximized quantum efficiency can also be achieved in the detector array.

Aerial radiation survey

International Nuclear Information System (INIS)

Pradeep Kumar, K.S.

1998-01-01

Aerial gamma spectrometry surveys are the most effective, comprehensive and preferred tool to delimit the large area surface contamination in a radiological emergency either due to a nuclear accident or following a nuclear strike. The airborne survey apart from providing rapid and economical evaluation of ground contamination over large areas due to larger ground clearance and higher speed, is the only technique to overcome difficulties posed by ground surveys of inaccessible region. The aerial survey technique can also be used for searching of lost radioactive sources, tracking of radioactive plume and generation of background data on the Emergency Planning Zone (EPZ) of nuclear installations

AERIAL RADIOLOGICAL SURVEYS

International Nuclear Information System (INIS)

Proctor, A.E.

1997-01-01

Measuring terrestrial gamma radiation from airborne platforms has proved to be a useful method for characterizing radiation levels over large areas. Over 300 aerial radiological surveys have been carried out over the past 25 years including U.S. Department of Energy (DOE) sites, commercial nuclear power plants, Formerly Utilized Sites Remedial Action Program/Uranium Mine Tailing Remedial Action Program (FUSRAP/UMTRAP) sites, nuclear weapons test sites, contaminated industrial areas, and nuclear accident sites. This paper describes the aerial measurement technology currently in use by the Remote Sensing Laboratory (RSL) for routine environmental surveys and emergency response activities. Equipment, data-collection and -analysis methods, and examples of survey results are described

Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment

Directory of Open Access Journals (Sweden)

Eija Honkavaara

2018-02-01

Full Text Available Unmanned airborne vehicles (UAV equipped with novel, miniaturized, 2D frame format hyper- and multispectral cameras make it possible to conduct remote sensing measurements cost-efficiently, with greater accuracy and detail. In the mapping process, the area of interest is covered by multiple, overlapping, small-format 2D images, which provide redundant information about the object. Radiometric correction of spectral image data is important for eliminating any external disturbance from the captured data. Corrections should include sensor, atmosphere and view/illumination geometry (bidirectional reflectance distribution function—BRDF related disturbances. An additional complication is that UAV remote sensing campaigns are often carried out under difficult conditions, with varying illumination conditions and cloudiness. We have developed a global optimization approach for the radiometric correction of UAV image blocks, a radiometric block adjustment. The objective of this study was to implement and assess a combined adjustment approach, including comprehensive consideration of weighting of various observations. An empirical study was carried out using imagery captured using a hyperspectral 2D frame format camera of winter wheat crops. The dataset included four separate flights captured during a 2.5 h time period under sunny weather conditions. As outputs, we calculated orthophoto mosaics using the most nadir images and sampled multiple-view hyperspectral spectra for vegetation sample points utilizing multiple images in the dataset. The method provided an automated tool for radiometric correction, compensating for efficiently radiometric disturbances in the images. The global homogeneity factor improved from 12–16% to 4–6% with the corrections, and a reduction in disturbances could be observed in the spectra of the object points sampled from multiple overlapping images. Residuals in the grey and white reflectance panels were less than 5% of the

Reconstructed Image Spatial Resolution of Multiple Coincidences Compton Imager

Science.gov (United States)

Andreyev, Andriy; Sitek, Arkadiusz; Celler, Anna

2010-02-01

We study the multiple coincidences Compton imager (MCCI) which is based on a simultaneous acquisition of several photons emitted in cascade from a single nuclear decay. Theoretically, this technique should provide a major improvement in localization of a single radioactive source as compared to a standard Compton camera. In this work, we investigated the performance and limitations of MCCI using Monte Carlo computer simulations. Spatial resolutions of the reconstructed point source have been studied as a function of the MCCI parameters, including geometrical dimensions and detector characteristics such as materials, energy and spatial resolutions.

AMS/NRCan Joint Survey Report: Aerial Campaign

International Nuclear Information System (INIS)

Wasiolek, Piotr; Stampahar, Jez; Malchow, Rusty; Stampahar, Tom; Lukens, Mike; Seywerd, Henry; Fortin, Richard; Harvey, Brad; Sinclair, Laurel

2014-01-01

In January 2014 the U.S. Department of Energy (DOE), National Nuclear Security Administration (NNSA) Aerial Measuring System (AMS) and the Natural Resources Canada (NRCan) Nuclear Emergency Response project conducted a series of joint surveys at a number of locations in Nevada including the Nevada National Security Site (NNSS). The goal of this project was to compare the responses of the two agencies' aerial radiation detection systems and data analysis techniques. This test included varied radioactive surface contamination levels and isotopic composition experienced at the NNSS and the differing data processing techniques utilized by the respective teams. Because both teams used the commercial aerial radiation detection systems from Radiation Solutions, Inc., the main focus of the campaign was to investigate the data acquisition techniques, data analysis, and ground-truth verification. The NRCan system consisted of four 4'' x 4'' x 16'' NaI(Tl) scintillator crystals of which two were externally mounted in a modified commercial cargo basket certified for the Eurocopter AS350; the NNSA AMS system consisted of twelve 2'' x 4'' x 16'' NaI(Tl) crystals in externally mounted dedicated pods. For NRCan, the joint survey provided an opportunity to characterize their system's response to extended sources of various fission products at the NNSS. Since both systems play an important role in their respective countries' national framework of radiological emergency response and are subject to multiple mutual cooperation agreements, it was important for each country to obtain more thorough knowledge of how they would employ these important assets and define the roles that they would each play in an actual response.

ENVIRONMENTAL CHANGES ANALYSIS IN BUCHAREST CITY USING CORONA, SPOT HRV AND IKONOS IMAGES

Directory of Open Access Journals (Sweden)

I. Noaje

2012-08-01

Full Text Available Bucharest, capital of Romania, deals with serious difficulties as a result of urban politics: influx of people due to industrialization and development of dormitory areas, lack of a modern infrastructure, absence of coherent and long term urban development politics, continuous depletion of environment. This paper presents a multisensor study relying on multiple data sets, both analogical and digital: satellite images (Corona – 1964 panchromatic, SPOT HRV – 1994 multispctral and panchromatic, IKONOS – 2007 multispectral, aerial photographs – 1994, complementary products (topographic and thematic maps. Georeferenced basis needs to be generated to highlight changes detection. The digital elevation model is generated from aerial photography 1:5,000 scaled, acquired in 1994. First a height correction is required followed by an affine transformation to the ground control points identified both in aerial photographs and IKONOS image. SPOT-HRV pansharpened satellite image has been rectified on georeferenced IKONOS image, by an affine transformation method. The Corona panoramic negative film was scanned and rubber sheeting method is used for rectification. The first 25 years of the study period (1964–1989 are characterized by growth of industrial areas, high density apartment buildings residential areas and leisure green areas by demolition of cultural heritage areas (hundred years old churches and architectural monuments. Changes between the imagery were determined partially through visual interpretation, using elements such as location, size, shape, shadow, tone, texture, and pattern (Corona image, partially using unsupervised classification (SPOT HRV and IKONOS. The second period of 18 years (1989–2007 highlighted considerable growth of residential areas in the city neighborhood, simultaneously with the diminish of green areas and massive deforestation in confiscated areas before and returned to the original owners.

Imaging of multiple endocrine neoplasia (MEN II A)

International Nuclear Information System (INIS)

Tanaka, Hiroko; Kohno, Atsushi; Nojiri, Yoko

1995-01-01

A retrospective review of diagnostic imaging findings of 20 cases of multiple endocrine neoplasia II A (MEN II A) was performed. The characteristic findings of thyroidal medullary carcinomas were relatively well-defined hypo- to isoechoic masses on US and coarse calcifications on plain X-ray. The pheochromocytomas were smaller in size and less enhancing than the sporadic ones, and they revealed marked high intensity on T2WI of MRI. We consider that these imaging findings were useful for the supplementary diagnosis of MEN II A. (author)

Low-dose multiple-information retrieval algorithm for X-ray grating-based imaging

International Nuclear Information System (INIS)

Wang Zhentian; Huang Zhifeng; Chen Zhiqiang; Zhang Li; Jiang Xiaolei; Kang Kejun; Yin Hongxia; Wang Zhenchang; Stampanoni, Marco

2011-01-01

The present work proposes a low dose information retrieval algorithm for X-ray grating-based multiple-information imaging (GB-MII) method, which can retrieve the attenuation, refraction and scattering information of samples by only three images. This algorithm aims at reducing the exposure time and the doses delivered to the sample. The multiple-information retrieval problem in GB-MII is solved by transforming a nonlinear equations set to a linear equations and adopting the nature of the trigonometric functions. The proposed algorithm is validated by experiments both on conventional X-ray source and synchrotron X-ray source, and compared with the traditional multiple-image-based retrieval algorithm. The experimental results show that our algorithm is comparable with the traditional retrieval algorithm and especially suitable for high Signal-to-Noise system.

Generating Impact Maps from Automatically Detected Bomb Craters in Aerial Wartime Images Using Marked Point Processes

Science.gov (United States)

Kruse, Christian; Rottensteiner, Franz; Hoberg, Thorsten; Ziems, Marcel; Rebke, Julia; Heipke, Christian

2018-04-01

The aftermath of wartime attacks is often felt long after the war ended, as numerous unexploded bombs may still exist in the ground. Typically, such areas are documented in so-called impact maps which are based on the detection of bomb craters. This paper proposes a method for the automatic detection of bomb craters in aerial wartime images that were taken during the Second World War. The object model for the bomb craters is represented by ellipses. A probabilistic approach based on marked point processes determines the most likely configuration of objects within the scene. Adding and removing new objects to and from the current configuration, respectively, changing their positions and modifying the ellipse parameters randomly creates new object configurations. Each configuration is evaluated using an energy function. High gradient magnitudes along the border of the ellipse are favored and overlapping ellipses are penalized. Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing provides the global energy optimum, which describes the conformance with a predefined model. For generating the impact map a probability map is defined which is created from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively. Our results show the general potential of the method for the automatic detection of bomb craters and its automated generation of an impact map in a heterogeneous image stock.

Challenges in clinical studies with multiple imaging probes

International Nuclear Information System (INIS)

Krohn, Kenneth A.; O'Sullivan, Finbarr; Crowley, John; Eary, Janet F.; Linden, Hannah M.; Link, Jeanne M.; Mankoff, David A.; Muzi, Mark; Rajendran, Joseph G.; Spence, Alexander M.; Swanson, Kristin R.

2007-01-01

This article addresses two related issues: (a) When a new imaging agent is proposed, how does the imager integrate it with other biomarkers, either sampled or imaged? (b) When we have multiple imaging agents, is the information additive or duplicative and how is this objectively determined? Molecular biology is leading to new treatment options with reduced normal tissue toxicity, and imaging should have a role in objectively evaluating new treatments. There are two roles for molecular characterization of disease. Molecular imaging measurements before therapy help predict the aggressiveness of disease and identify therapeutic targets and, therefore, help choose the optimal therapy for an individual. Measurements of specific biochemical processes made during or after therapy should be sensitive measures of tumor response. The rules of evidence are not fully developed for the prognostic role of imaging biomarkers, but the potential of molecular imaging provides compelling motivation to push forward with convincing validation studies. New imaging procedures need to be characterized for their effectiveness under realistic clinical conditions to improve the management of patients and achieve a better outcome. The purpose of this article is to promote a critical discussion within the molecular imaging community because our future value to the overall biomedical community will be in supporting better treatment outcomes rather than in detection

GEOPOSITIONING PRECISION ANALYSIS OF MULTIPLE IMAGE TRIANGULATION USING LRO NAC LUNAR IMAGES

Directory of Open Access Journals (Sweden)

K. Di

2016-06-01

Full Text Available This paper presents an empirical analysis of the geopositioning precision of multiple image triangulation using Lunar Reconnaissance Orbiter Camera (LROC Narrow Angle Camera (NAC images at the Chang’e-3(CE-3 landing site. Nine LROC NAC images are selected for comparative analysis of geopositioning precision. Rigorous sensor models of the images are established based on collinearity equations with interior and exterior orientation elements retrieved from the corresponding SPICE kernels. Rational polynomial coefficients (RPCs of each image are derived by least squares fitting using vast number of virtual control points generated according to rigorous sensor models. Experiments of different combinations of images are performed for comparisons. The results demonstrate that the plane coordinates can achieve a precision of 0.54 m to 2.54 m, with a height precision of 0.71 m to 8.16 m when only two images are used for three-dimensional triangulation. There is a general trend that the geopositioning precision, especially the height precision, is improved with the convergent angle of the two images increasing from several degrees to about 50°. However, the image matching precision should also be taken into consideration when choosing image pairs for triangulation. The precisions of using all the 9 images are 0.60 m, 0.50 m, 1.23 m in along-track, cross-track, and height directions, which are better than most combinations of two or more images. However, triangulation with selected fewer images could produce better precision than that using all the images.

Spike Neuromorphic VLSI-Based Bat Echolocation for Micro-Aerial Vehicle Guidance

Science.gov (United States)

2007-03-31

IFinal 03/01/04 - 02/28/07 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Neuromorphic VLSI-based Bat Echolocation for Micro-aerial 5b.GRANTNUMBER Vehicle...uncovered interesting new issues in our choice for representing the intensity of signals. We have just finished testing the first chip version of an echo...timing-based algorithm (’openspace’) for sonar-guided navigation amidst multiple obstacles. 15. SUBJECT TERMS Neuromorphic VLSI, bat echolocation

Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems

Science.gov (United States)

Longmore, S. N.; Collins, R. P.; Pfeifer, S.; Fox, S. E.; Mulero-Pazmany, M.; Bezombes, F.; Goodwind, A.; de Juan Ovelar, M.; Knapen, J. H.; Wich, S. A.

2017-02-01

In this paper we describe an unmanned aerial system equipped with a thermal-infrared camera and software pipeline that we have developed to monitor animal populations for conservation purposes. Taking a multi-disciplinary approach to tackle this problem, we use freely available astronomical source detection software and the associated expertise of astronomers, to efficiently and reliably detect humans and animals in aerial thermal-infrared footage. Combining this astronomical detection software with existing machine learning algorithms into a single, automated, end-to-end pipeline, we test the software using aerial video footage taken in a controlled, field-like environment. We demonstrate that the pipeline works reliably and describe how it can be used to estimate the completeness of different observational datasets to objects of a given type as a function of height, observing conditions etc. - a crucial step in converting video footage to scientifically useful information such as the spatial distribution and density of different animal species. Finally, having demonstrated the potential utility of the system, we describe the steps we are taking to adapt the system for work in the field, in particular systematic monitoring of endangered species at National Parks around the world.

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

Directory of Open Access Journals (Sweden)

Fan Zhang

2016-04-01

Full Text Available With the development of synthetic aperture radar (SAR technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO. However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing.

Science.gov (United States)

Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin

2016-04-07

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Segmentation of Shadowed Buildings in Dense Urban Areas from Aerial Photographs

OpenAIRE

Susaki, Junichi

2012-01-01

Segmentation of buildings in urban areas, especially dense urban areas, by using remotely sensed images is highly desirable. However, segmentation results obtained by using existing algorithms are unsatisfactory because of the unclear boundaries between buildings and the shadows cast by neighboring buildings. In this paper, an algorithm is proposed that successfully segments buildings from aerial photographs, including shadowed buildings in dense urban areas. To handle roofs having rough text...

Using IKONOS and Aerial Videography to Validate Landsat Land Cover Maps of Central African Tropical Rain Forests

Science.gov (United States)

Lin, T.; Laporte, N. T.

2003-12-01

Compared to the traditional validation methods, aerial videography is a relatively inexpensive and time-efficient approach to collect "field" data for validating satellite-derived land cover map over large areas. In particular, this approach is valuable in remote and inaccessible locations. In the Sangha Tri-National Park region of Central Africa, where road access is limited to industrial logging sites, we are using IKONOS imagery and aerial videography to assess the accuracy of Landsat-derived land cover maps. As part of a NASA Land Cover Land Use Change project (INFORMS) and in collaboration with the Wildlife Conservation Society in the Republic of Congo, over 1500km of aerial video transects were collected in the Spring of 2001. The use of MediaMapper software combined with a VMS 200 video mapping system enabled the collection of aerial transects to be registered with geographic locations from a Geographic Positioning System. Video frame were extracted, visually interpreted, and compared to land cover types mapped by Landsat. We addressed the limitations of accuracy assessment using aerial-base data and its potential for improving vegetation mapping in tropical rain forests. The results of the videography and IKONOS image analysis demonstrate the utility of very high resolution imagery for map validation and forest resource assessment.

Multiple-targeted graphene-based nanocarrier for intracellular imaging of mRNAs

International Nuclear Information System (INIS)

Wang, Ying; Li, Zhaohui; Liu, Misha; Xu, Jinjin; Hu, Dehong; Lin, Yuehe; Li, Jinghong

2017-01-01

Simultaneous detection and imaging of multiple intracellular messenger RNA (mRNAs) hold great significant for early cancer diagnostics and preventive medicine development. Herein, we propose a multiple-targeted graphene oxide (GO) nanocarrier that can simultaneously detect and image different type mRNAs in living cells. First of all, in vitro detection of multiple targets have been realized successfully based on the multiple-targeted GO nanocarrier with linear relationship ranging from 3 nM to 200 nM, as well as sensitive detection limit of 1.84 nM for manganese superoxide dismutase (Mn-SOD) mRNA and 2.45 nM for β-actin mRNA. Additionally, this nanosensing platform composed of fluorescent labelled single strand DNA probes and GO nanocarrier can identify Mn-SOD mRNA and endogenous mRNA of β-actin in living cancer cells, showing rapid response, high specificity, nuclease stability, and good biocompatibility during the cell imaging. Thirdly, changes of the expression levels of mRNA in living cells before or after the drug treatment can be monitored successfully. By using multiple ssDNA as probes and GO nanocarrier as the cellular delivery cargo, the proposed simultaneous multiple-targeted sensing platform will be of great potential as a powerful tool for intracellular trafficking process from basic research to clinical diagnosis. - Graphical abstract: Schematic illustration of simultaneously multiple mRNAs monitoring inside single living breast cancer cell based on GO nanocarrier. In particular, the fluorescent signals could be monitored when Mn-SOD probe (red) and β-actin probe (green) hybridizes with their mRNA targets inside the living cells. Random probe (orange) was regarded as control probe for the sensing strategy. - Highlights: • A multiple-targeted GO nanocarrier was used for mRNAs imaging and expression changes after drug treatment can be monitored successfully. • Sensitive detection limit of 1.84 nM for manganese superoxide dismutase (Mn-SOD) m

Yellow River Icicle Hazard Dynamic Monitoring Using UAV Aerial Remote Sensing Technology

International Nuclear Information System (INIS)

Wang, H B; Wang, G H; Tang, X M; Li, C H

2014-01-01

Monitoring the response of Yellow River icicle hazard change requires accurate and repeatable topographic surveys. A new method based on unmanned aerial vehicle (UAV) aerial remote sensing technology is proposed for real-time data processing in Yellow River icicle hazard dynamic monitoring. The monitoring area is located in the Yellow River ice intensive care area in southern BaoTou of Inner Mongolia autonomous region. Monitoring time is from the 20th February to 30th March in 2013. Using the proposed video data processing method, automatic extraction covering area of 7.8 km 2 of video key frame image 1832 frames took 34.786 seconds. The stitching and correcting time was 122.34 seconds and the accuracy was better than 0.5 m. Through the comparison of precise processing of sequence video stitching image, the method determines the change of the Yellow River ice and locates accurate positioning of ice bar, improving the traditional visual method by more than 100 times. The results provide accurate aid decision information for the Yellow River ice prevention headquarters. Finally, the effect of dam break is repeatedly monitored and ice break five meter accuracy is calculated through accurate monitoring and evaluation analysis

Aerial service robotics: the AIRobots perspective

NARCIS (Netherlands)

Marconi, L.; Basile, F.; Caprari, G.; Carloni, Raffaella; Chiacchio, P.; Hurzeler, C.; Lippiello, V.; Naldi, R.; Siciliano, B.; Stramigioli, Stefano; Zwicker, E.

This paper presents the main vision and research activities of the ongoing European project AIRobots (Innova- tive Aerial Service Robot for Remote Inspection by Contact, www.airobots.eu). The goal of AIRobots is to develop a new generation of aerial service robots capable of supporting human beings

Efficient Forest Fire Detection Index for Application in Unmanned Aerial Systems (UASs

Directory of Open Access Journals (Sweden)

Henry Cruz

2016-06-01

Full Text Available This article proposes a novel method for detecting forest fires, through the use of a new color index, called the Forest Fire Detection Index (FFDI, developed by the authors. The index is based on methods for vegetation classification and has been adapted to detect the tonalities of flames and smoke; the latter could be included adaptively into the Regions of Interest (RoIs with the help of a variable factor. Multiple tests have been performed upon database imagery and present promising results: a detection precision of 96.82% has been achieved for image sizes of 960 × 540 pixels at a processing time of 0.0447 seconds. This achievement would lead to a performance of 22 f/s, for smaller images, while up to 54 f/s could be reached by maintaining a similar detection precision. Additional tests have been performed on fires in their early stages, achieving a precision rate of p = 96.62%. The method could be used in real-time in Unmanned Aerial Systems (UASs, with the aim of monitoring a wider area than through fixed surveillance systems. Thus, it would result in more cost-effective outcomes than conventional systems implemented in helicopters or satellites. UASs could also reach inaccessible locations without jeopardizing people’s safety. On-going work includes implementation into a commercially available drone.

WOODLAND MAPPING AT SINGLE-TREE LEVELS USING OBJECT-ORIENTED CLASSIFICATION OF UNMANNED AERIAL VEHICLE (UAV IMAGES

Directory of Open Access Journals (Sweden)

A. Chenari

2017-09-01

Full Text Available Remotely sensed datasets offer a reliable means to precisely estimate biophysical characteristics of individual species sparsely distributed in open woodlands. Moreover, object-oriented classification has exhibited significant advantages over different classification methods for delineation of tree crowns and recognition of species in various types of ecosystems. However, it still is unclear if this widely-used classification method can have its advantages on unmanned aerial vehicle (UAV digital images for mapping vegetation cover at single-tree levels. In this study, UAV orthoimagery was classified using object-oriented classification method for mapping a part of wild pistachio nature reserve in Zagros open woodlands, Fars Province, Iran. This research focused on recognizing two main species of the study area (i.e., wild pistachio and wild almond and estimating their mean crown area. The orthoimage of study area was consisted of 1,076 images with spatial resolution of 3.47 cm which was georeferenced using 12 ground control points (RMSE=8 cm gathered by real-time kinematic (RTK method. The results showed that the UAV orthoimagery classified by object-oriented method efficiently estimated mean crown area of wild pistachios (52.09±24.67 m2 and wild almonds (3.97±1.69 m2 with no significant difference with their observed values (α=0.05. In addition, the results showed that wild pistachios (accuracy of 0.90 and precision of 0.92 and wild almonds (accuracy of 0.90 and precision of 0.89 were well recognized by image segmentation. In general, we concluded that UAV orthoimagery can efficiently produce precise biophysical data of vegetation stands at single-tree levels, which therefore is suitable for assessment and monitoring open woodlands.

Determination of Exterior Orientation Parameters Through Direct Geo-Referencing in a Real-Time Aerial Monitoring System

Science.gov (United States)

Kim, H.; Lee, J.; Choi, K.; Lee, I.

2012-07-01

Rapid responses for emergency situations such as natural disasters or accidents often require geo-spatial information describing the on-going status of the affected area. Such geo-spatial information can be promptly acquired by a manned or unmanned aerial vehicle based multi-sensor system that can monitor the emergent situations in near real-time from the air using several kinds of sensors. Thus, we are in progress of developing such a real-time aerial monitoring system (RAMS) consisting of both aerial and ground segments. The aerial segment acquires the sensory data about the target areas by a low-altitude helicopter system equipped with sensors such as a digital camera and a GPS/IMU system and transmits them to the ground segment through a RF link in real-time. The ground segment, which is a deployable ground station installed on a truck, receives the sensory data and rapidly processes them to generate ortho-images, DEMs, etc. In order to generate geo-spatial information, in this system, exterior orientation parameters (EOP) of the acquired images are obtained through direct geo-referencing because it is difficult to acquire coordinates of ground points in disaster area. The main process, since the data acquisition stage until the measurement of EOP, is discussed as follows. First, at the time of data acquisition, image acquisition time synchronized by GPS time is recorded as part of image file name. Second, the acquired data are then transmitted to the ground segment in real-time. Third, by processing software for ground segment, positions/attitudes of acquired images are calculated through a linear interpolation using the GPS time of the received position/attitude data and images. Finally, the EOPs of images are obtained from position/attitude data by deriving the relationships between a camera coordinate system and a GPS/IMU coordinate system. In this study, we evaluated the accuracy of the EOP decided by direct geo-referencing in our system. To perform this

DETERMINATION OF EXTERIOR ORIENTATION PARAMETERS THROUGH DIRECT GEO-REFERENCING IN A REAL-TIME AERIAL MONITORING SYSTEM

Directory of Open Access Journals (Sweden)

H. Kim

2012-07-01

Full Text Available Rapid responses for emergency situations such as natural disasters or accidents often require geo-spatial information describing the on-going status of the affected area. Such geo-spatial information can be promptly acquired by a manned or unmanned aerial vehicle based multi-sensor system that can monitor the emergent situations in near real-time from the air using several kinds of sensors. Thus, we are in progress of developing such a real-time aerial monitoring system (RAMS consisting of both aerial and ground segments. The aerial segment acquires the sensory data about the target areas by a low-altitude helicopter system equipped with sensors such as a digital camera and a GPS/IMU system and transmits them to the ground segment through a RF link in real-time. The ground segment, which is a deployable ground station installed on a truck, receives the sensory data and rapidly processes them to generate ortho-images, DEMs, etc. In order to generate geo-spatial information, in this system, exterior orientation parameters (EOP of the acquired images are obtained through direct geo-referencing because it is difficult to acquire coordinates of ground points in disaster area. The main process, since the data acquisition stage until the measurement of EOP, is discussed as follows. First, at the time of data acquisition, image acquisition time synchronized by GPS time is recorded as part of image file name. Second, the acquired data are then transmitted to the ground segment in real-time. Third, by processing software for ground segment, positions/attitudes of acquired images are calculated through a linear interpolation using the GPS time of the received position/attitude data and images. Finally, the EOPs of images are obtained from position/attitude data by deriving the relationships between a camera coordinate system and a GPS/IMU coordinate system. In this study, we evaluated the accuracy of the EOP decided by direct geo-referencing in our system

Motion Component Supported Boosted Classifier for CAR Detection in Aerial Imagery

Science.gov (United States)

Tuermer, S.; Leitloff, J.; Reinartz, P.; Stilla, U.

2011-04-01

Research of automatic vehicle detection in aerial images has been done with a lot of innovation and constantly rising success for years. However information was mostly taken from a single image only. Our aim is using the additional information which is offered by the temporal component, precisely the difference of the previous and the consecutive image. On closer viewing the moving objects are mainly vehicles and therefore we provide a method which is able to limit the search space of the detector to changed areas. The actual detector is generated of HoG features which are composed and linearly weighted by AdaBoost. Finally the method is tested on a motorway section including an exit and congested traffic near Munich, Germany.

A REVIEW OF TACTICAL UNMANNED AERIAL VEHICLE DESIGN STUDIES

OpenAIRE

Coban, Sezer; Oktay, Tugrul

2017-01-01

In this study, a literaturesearch was conducted on tactical unmanned aerial vehicles. First of all, it wasclassified as an unmanned aerial vehicle. It is mentioned about thecharacteristics of ZANKA-III, which is highly autonomous, passive and activemorphing, aerodynamically perfect, tactical unmanned aerial vehicle (TUAV)ZANKA-III, supported by TUBITAK's 1001 Ardeb program 115M603 by TUBITAK and itis mentioned that they have superior characteristics from other tacticalunmanned aerial veh...

3D MODELLING AND ACCURACY ASSESSMENT OF GRANITE QUARRY USING UNMMANNED AERIAL VEHICLE

Directory of Open Access Journals (Sweden)

D. González-Aguilera

2012-07-01

Full Text Available The unmanned aerial vehicles (UAVs are automated systems whose main characteristic is that can be remotely piloted. This property is especially interesting in those civil engineering works in which the accuracy of the model is not reachable by common aerial or satellite systems, there is a difficult accessibility to the infrastructure due to location and geometry aspects, and the economic resources are limited. This paper aims to show the research, development and application of a UAV that will generate georeferenced spatial information at low cost, high quality, and high availability. In particular, a 3D modelling and accuracy assessment of granite quarry using UAV is applied. With regard to the image-based modelling pipeline, an automatic approach supported by open source tools is performed. The process encloses the well-known image-based modelling steps: calibration, extraction and matching of features; relative and absolute orientation of images and point cloud and surface generation. Beside this, an assessment of the final model accuracy is carried out by means of terrestrial laser scanner (TLS, imaging total station (ITS and global navigation satellite system (GNSS in order to ensure its validity. This step follows a twofold approach: (i firstly, using singular check points to provide a dimensional control of the model and (ii secondly, analyzing the level of agreement between the realitybased 3D model obtained from UAV and the generated with TLS. The main goal is to establish and validate an image-based modelling workflow using UAV technology which can be applied in the surveying and monitoring of different quarries.

Multiple images of our galaxy in closed, multiply connected cosmologies

International Nuclear Information System (INIS)

Fagundes, H.V.

1985-01-01

Friedmanian cosmology with multiply connected spatial sections allows multiple images of cosmic sources, in particular of the galaxy itself. This is illustrated with a specific example of a closed hyperbolic model and a brief mention of a spherical model. Such images may eventually become observable (or recognized as such), thus providing a new test of relativistic cosmology. (Author) [pt

Multiple-image encryption via lifting wavelet transform and XOR operation based on compressive ghost imaging scheme

Science.gov (United States)

Li, Xianye; Meng, Xiangfeng; Yang, Xiulun; Wang, Yurong; Yin, Yongkai; Peng, Xiang; He, Wenqi; Dong, Guoyan; Chen, Hongyi

2018-03-01

A multiple-image encryption method via lifting wavelet transform (LWT) and XOR operation is proposed, which is based on a row scanning compressive ghost imaging scheme. In the encryption process, the scrambling operation is implemented for the sparse images transformed by LWT, then the XOR operation is performed on the scrambled images, and the resulting XOR images are compressed in the row scanning compressive ghost imaging, through which the ciphertext images can be detected by bucket detector arrays. During decryption, the participant who possesses his/her correct key-group, can successfully reconstruct the corresponding plaintext image by measurement key regeneration, compression algorithm reconstruction, XOR operation, sparse images recovery, and inverse LWT (iLWT). Theoretical analysis and numerical simulations validate the feasibility of the proposed method.

New Hybrid Variational Recovery Model for Blurred Images with Multiplicative Noise

DEFF Research Database (Denmark)

Dong, Yiqiu; Zeng, Tieyong

2013-01-01

A new hybrid variational model for recovering blurred images in the presence of multiplicative noise is proposed. Inspired by previous work on multiplicative noise removal, an I-divergence technique is used to build a strictly convex model under a condition that ensures the uniqueness...

Evaluating EUV mask pattern imaging with two EUV microscopes

International Nuclear Information System (INIS)

Goldberg, Kenneth A.; Takase, Kei; Naulleau, Patrick P.; Han, Hakseung; Barty, Anton; Kinoshita, Hiroo; Hamamoto, Kazuhiro

2008-01-01

Aerial image measurement plays a key role in the development of patterned reticles for each generation of lithography. Studying the field transmitted (reflected) from EUV masks provides detailed information about potential disruptions caused by mask defects, and the performance of defect repair strategies, without the complications of photoresist imaging. Furthermore, by measuring the continuously varying intensity distribution instead of a thresholded, binary resist image, aerial image measurement can be used as feedback to improve mask and lithography system modeling methods. Interest in EUV, at-wavelength, aerial image measurement lead to the creation of several research tools worldwide. These tools are used in advanced mask development work, and in the evaluation of the need for commercial at-wavelength inspection tools. They describe performance measurements of two such tools, inspecting the same EUV mask in a series of benchmarking tests that includes brightfield and darkfield patterns. One tool is the SEMATECH Berkeley Actinic Inspection Tool (AIT) operating on a bending magnet beamline at Lawrence Berkeley National Laboratory's Advanced Light Source. The AIT features an EUV Fresnel zoneplate microscope that emulates the numerical aperture of a 0.25-NA stepper, and projects the aerial image directly onto a CCD camera, with 700x magnification. The second tool is an EUV microscope (EUVM) operating at the NewSUBARU synchrotron in Hyogo, Japan. The NewSUBARU tool projects the aerial image using a reflective, 30x Schwarzschild objective lens, followed by a 10-200x x-ray zooming tube. The illumination conditions and the imaging etendue are different for the two tools. The benchmarking measurements were used to determine many imaging and performance properties of the tools, including resolution, modulation transfer function (MTF), aberration magnitude, aberration field-dependence (including focal-plane tilt), illumination uniformity, line-edge roughness, and flare

The effective use of unmanned aerial vehicles for local law enforcement

Science.gov (United States)

Gasque, Leighton

This qualitative study was done to interview local law enforcement in Murfreesboro, Tennessee to determine if unmanned aerial vehicles could increase the safety of policy officers. Many police officers face dangerous scenarios on a daily basis; however, officers must also perform non-criminal related responsibilities that could put them in hazardous situations. UAVs have multiple capabilities that can decrease the number of hazards in an emergency situation whether it is environmental, traffic related, criminal activity, or investigations. Officers were interviewed to find whether or not unmanned aerial vehicles (UAV) could be useful manpower on the police force. The study was also used to find whether or not officers foresee UAVs being used in law enforcement. The study revealed that UAVs could be used to add useful manpower to law enforcement based on the capabilities a UAV may have. Police officers cannot confirm whether or not they would be able to use a UAV until further research is conducted to examine the relation of costs to usage.

Hybrid System Design for the Coordination of Multi-Modal Aerial Robots

DEFF Research Database (Denmark)

Koo, T. John; Quottrup, Michael Melholt; Clifton, C. A.

2006-01-01

In this paper we provide a framework for the coordination of a network of heterogeneous aerial robots by using temporal logic to formulate mission speci¯cations for the network of robots. The full dynamics of the aerial robots are considered, and multiple controllers that can cope with various......¯ed. These robots are coordinated by communicating through a single occupancy table. By using the model checker Uppaal, a discrete plan that satis¯es a given temporal logic formula, speci¯ed in CTL, is generated for the robot to execute. Finally, the discrete plan for each robot is re¯ned into a discrete control...... constraints are designed to ensure that desired reachability properties can be preserved by properly switching among the controllers. A timed automaton is then constructed for preserving the temporal properties of a given robot. For di®erent types of robots, unique temporal properties can be speci...

An Improved Image Encryption Algorithm Based on Cyclic Rotations and Multiple Chaotic Sequences: Application to Satellite Images

Directory of Open Access Journals (Sweden)

MADANI Mohammed

2017-10-01

Full Text Available In this paper, a new satellite image encryption algorithm based on the combination of multiple chaotic systems and a random cyclic rotation technique is proposed. Our contribution consists in implementing three different chaotic maps (logistic, sine, and standard combined to improve the security of satellite images. Besides enhancing the encryption, the proposed algorithm also focuses on advanced efficiency of the ciphered images. Compared with classical encryption schemes based on multiple chaotic maps and the Rubik's cube rotation, our approach has not only the same merits of chaos systems like high sensitivity to initial values, unpredictability, and pseudo-randomness, but also other advantages like a higher number of permutations, better performances in Peak Signal to Noise Ratio (PSNR and a Maximum Deviation (MD.

CERN: an aerial view

CERN Multimedia

2004-01-01

On 30th January, when CERN still resembled a winter wonderland, a helicopter with a photographer on board took off on an aerial tour. One sunny morning at the end of January, when the area was waking up to an overnight snowfall, a helicopter took off from the Meyrin site with a CERN photographer on board. CERN has been the subject of aerial photographs ever since its creation. Although its appearance has changed over the years, the Laboratory has aged well. The aerial photographs taken during its fifty-year history bear witness to its expansion, showing how a handful of buildings and a first accelerator have blossomed into an entire machine complex. Let's take to the skies and have a look at some of the photos taken on this crisp January morning: a sight for sore eyes! In the foreground, Building 40 on the Meyrin site is recognisable from its magnet shape.On the right of the Route de Meyrin (crossing the photo diagonally), next to Point 1, the work on the Globe of Innovation, which got underway at the beg...

1988 Mosaic of Aerial Photography of the Salt River Bay National Historical Park and Ecological Preserve, St. Croix, USVI

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Aerial photographs taken by NOAA's National Geodetic Survey during 1988 were mosaicked and orthorectified by the Biogeography Program. The resulting image was used...

2000 Mosaic of Aerial Photography of the Salt River Bay National Historical Park and Ecological Preserve, St. Croix, USVI

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Aerial photographs taken by NOAA's National Geodetic Survey during 2000 were mosaicked and orthorectified by the Biogeography Program. The resulting image was used...

1954 Lea County DHO Aerial Photo Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

Index for SCS 1934-1937 Aerial Photography

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

1941 Otero County CTF Aerial Photo Idnex

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

1946 Whitewater-Animas DDR Aerial Photo Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

1947 Sandoval County DFD Aerial Photo Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

1946 Eddy County DEO Aerial Photo Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

1936 Curry County AG Aerial Photo Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

MR imaging studies of multiple myeloma in the vertebral column

International Nuclear Information System (INIS)

Albert, S.; Leeds, N.E.

1990-01-01

This paper studies the sensitivity and characteristics of MR imaging in the diagnosis of myeloma in the vertebral column. The cervical, thoracic, and lumbar spines of 12 patients with known multiple myeloma were imaged with small flip angle, fast gradient-echo, proton-density (FPD) as well as spin-echo T1-weighted, T2-weighted, and intermediate (SE 2,000/20-30) imaging. The FPD images were acquired with pulse sequence gradient recalled acquisition in a steady state at a magnetic field strength of 1.5T with use of a license-plate and a circular surface coil

Preliminary Design of Aerial Spraying System for Microlight Aircraft

Science.gov (United States)

Omar, Zamri; Idris, Nurfazliawati; Rahim, M. Zulafif

2017-10-01

Undoubtedly agricultural is an important sector because it provides essential nutrients for human, and consequently is among the biggest sector for economic growth worldwide. It is crucial to ensure crops production is protected from any plant diseases and pests. Thus aerial spraying system on crops is developed to facilitate farmers to for crops pests control and it is very effective spraying method especially for large and hilly crop areas. However, the use of large aircraft for aerial spaying has a relatively high operational cost. Therefore, microlight aircraft is proposed to be used for crops aerial spraying works for several good reasons. In this paper, a preliminary design of aerial spraying system for microlight aircraft is proposed. Engineering design methodology is adopted in the development of the aerial sprayer and steps involved design are discussed thoroughly. A preliminary design for the microlight to be attached with an aerial spraying system is proposed.

MR imaging of multiple sclerosis in patients with negative cerebrospinal fluid

International Nuclear Information System (INIS)

Dooms, G.C.; Mathurin, P.; Cornelis, G.; Laterre, E.C.; Demeure, R.

1986-01-01

A prospective study was performed to assess the value of MR imaging for detecting demyelinating disease of the brain in 50 patients with clinically suspected multiple sclerosis but negative cerebrospinal fluid (CSF). The MR imaging examinations were performed with a superconducting magnet (Philips Gyroscan S15) operating at 0.5T. A multisection, double spin-echo technique was used in all cases (TR = 2,100 msec, TE = 50 and 100 msec). No abnormality was demonstrated in eight patients. In the others, lesions were usually located in the periventricular white matter (rounded masses and/or high signal intensity bands along the lateral ventricles), the brain stem and thalami (12 patients), and the cerebellum (6 patients). In conclusion, MR imaging appears to be an exquisite imaging modality for confirmation of clinically suspected multiple sclerosis in patients with negative CSF. However, it must include examination of the spinal cord when the brain examination is negative

Contribution of magnetic resonance imaging to the diagnosis of multiple sclerosis

International Nuclear Information System (INIS)

Seidl, Z.; Obenberger, J.; Vitak, T.

1996-01-01

The potential of magnetic resonance imaging in the diagnosis of multiple sclerosis (MS) was confirmed on 52 patients. In 25 patients, MS was diagnosed as highly probable, in additional 8 patients this diagnosis was suspected. MR imaging supported the diagnosis in 21 (95%) patients where this disease had been diagnosed as highly probable, and in 3 (38%) suspect patients. Lesions were found most frequently paraventricularly in the white matter of the brain, but also in the deep structures of the white matter of the temporal lobe and below the tentorium (in the cerebellum, pons and mesencephalon). No lesions were found in the optic nerve despite the frequent diagnosis of retrobulbar neuritis. Computerized tomography (CT) was performed in 14 patients; this technique only supported the diagnosis of MS in 3 patients, in all of whom this diagnosis had also been suggested by MR imaging. It is concluded that MR imaging can fully supersede CT as a tool for diagnosing multiple sclerosis. 3 figs., 10 refs

Probabilistic images (PBIS): A concise image representation technique for multiple parameters

International Nuclear Information System (INIS)

Wu, L.C.; Yeh, S.H.; Chen, Z.; Liu, R.S.

1984-01-01

Based on m parametric images (PIs) derived from a dynamic series (DS), each pixel of DS is regarded as an m-dimensional vector. Given one set of normal samples (pixels) N and another of abnormal samples A, probability density functions (pdfs) of both sets are estimated. Any unknown sample is classified into N or A by calculating the probability of its being in the abnormal set using the Bayes' theorem. Instead of estimating the multivariate pdfs, a distance ratio transformation is introduced to map the m-dimensional sample space to one dimensional Euclidean space. Consequently, the image that localizes the regional abnormalities is characterized by the probability of being abnormal. This leads to the new representation scheme of PBIs. Tc-99m HIDA study for detecting intrahepatic lithiasis (IL) was chosen as an example of constructing PBI from 3 parameters derived from DS and such a PBI was compared with those 3 PIs, namely, retention ratio image (RRI), peak time image (TNMAX) and excretion mean transit time image (EMTT). 32 normal subjects and 20 patients with proved IL were collected and analyzed. The resultant sensitivity and specificity of PBI were 97% and 98% respectively. They were superior to those of any of the 3 PIs: RRI (94/97), TMAX (86/88) and EMTT (94/97). Furthermore, the contrast of PBI was much better than that of any other image. This new image formation technique, based on multiple parameters, shows the functional abnormalities in a structural way. Its good contrast makes the interpretation easy. This technique is powerful compared to the existing parametric image method

Benefits and limitations of imaging multiples: Interferometric and resonant migration

KAUST Repository

Guo, Bowen

2015-07-01

The benefits and limitations of imaging multiples are reviewed for interferometric migration and resonant migration. Synthetic and field data examples are used to characterize the effectiveness of the methods.

Benefits and limitations of imaging multiples: Interferometric and resonant migration

KAUST Repository

Guo, Bowen; Yu, Jianhua; Huang, Yunsong; Hanafy, Sherif M.; Schuster, Gerard T.

2015-01-01

The benefits and limitations of imaging multiples are reviewed for interferometric migration and resonant migration. Synthetic and field data examples are used to characterize the effectiveness of the methods.

Automated hotspot analysis with aerial image CD metrology for advanced logic devices

Science.gov (United States)

Buttgereit, Ute; Trautzsch, Thomas; Kim, Min-ho; Seo, Jung-Uk; Yoon, Young-Keun; Han, Hak-Seung; Chung, Dong Hoon; Jeon, Chan-Uk; Meyers, Gary

2014-09-01

Continuously shrinking designs by further extension of 193nm technology lead to a much higher probability of hotspots especially for the manufacturing of advanced logic devices. The CD of these potential hotspots needs to be precisely controlled and measured on the mask. On top of that, the feature complexity increases due to high OPC load in the logic mask design which is an additional challenge for CD metrology. Therefore the hotspot measurements have been performed on WLCD from ZEISS, which provides the benefit of reduced complexity by measuring the CD in the aerial image and qualifying the printing relevant CD. This is especially of advantage for complex 2D feature measurements. Additionally, the data preparation for CD measurement becomes more critical due to the larger amount of CD measurements and the increasing feature diversity. For the data preparation this means to identify these hotspots and mark them automatically with the correct marker required to make the feature specific CD measurement successful. Currently available methods can address generic pattern but cannot deal with the pattern diversity of the hotspots. The paper will explore a method how to overcome those limitations and to enhance the time-to-result in the marking process dramatically. For the marking process the Synopsys WLCD Output Module was utilized, which is an interface between the CATS mask data prep software and the WLCD metrology tool. It translates the CATS marking directly into an executable WLCD measurement job including CD analysis. The paper will describe the utilized method and flow for the hotspot measurement. Additionally, the achieved results on hotspot measurements utilizing this method will be presented.

A Low Cost Rokkaku Kite Setup for Aerial Photogrammetric System

Science.gov (United States)

Khan, A. F.; Khurshid, K.; Saleh, N.; Yousuf, A. A.

2015-03-01

Orthogonally Projected Area (OPA) of a geographical feature has primarily been studied utilizing rather time consuming field based sampling techniques. Remote sensing on the contrary provides the ability to acquire large scale data at a snapshot of time and lets the OPA to be calculated conveniently and with reasonable accuracy. Unfortunately satellite based remote sensing provides data at high cost and limited spatial resolution for scientific studies focused at small areas such as micro lakes micro ecosystems, etc. More importantly, recent satellite data may not be readily available for a particular location. This paper describes a low cost photogrammetric system to measure the OPA of a small scale geographic feature such as a plot of land, micro lake or an archaeological site, etc. Fitted with a consumer grade digital imaging system, a Rokkaku kite aerial platform with stable flight characteristics is designed and fabricated for image acquisition. The data processing procedure involves automatic Ground Control Point (GCP) detection, intelligent target area shape determination with minimal human input. A Graphical User Interface (GUI) is built from scratch in MATLAB to allow the user to conveniently process the acquired data, archive and retrieve the results. Extensive on-field experimentation consists of multiple geographic features including flat land surfaces, buildings, undulating rural areas, and an irregular shaped micro lake, etc. Our results show that the proposed system is not only low cost, but provides a framework that is easy and fast to setup while maintaining the required constraints on the accuracy.

Configuration and specifications of an Unmanned Aerial Vehicle (UAV for early site specific weed management.

Directory of Open Access Journals (Sweden)

Jorge Torres-Sánchez

Full Text Available A new aerial platform has risen recently for image acquisition, the Unmanned Aerial Vehicle (UAV. This article describes the technical specifications and configuration of a UAV used to capture remote images for early season site- specific weed management (ESSWM. Image spatial and spectral properties required for weed seedling discrimination were also evaluated. Two different sensors, a still visible camera and a six-band multispectral camera, and three flight altitudes (30, 60 and 100 m were tested over a naturally infested sunflower field. The main phases of the UAV workflow were the following: 1 mission planning, 2 UAV flight and image acquisition, and 3 image pre-processing. Three different aspects were needed to plan the route: flight area, camera specifications and UAV tasks. The pre-processing phase included the correct alignment of the six bands of the multispectral imagery and the orthorectification and mosaicking of the individual images captured in each flight. The image pixel size, area covered by each image and flight timing were very sensitive to flight altitude. At a lower altitude, the UAV captured images of finer spatial resolution, although the number of images needed to cover the whole field may be a limiting factor due to the energy required for a greater flight length and computational requirements for the further mosaicking process. Spectral differences between weeds, crop and bare soil were significant in the vegetation indices studied (Excess Green Index, Normalised Green-Red Difference Index and Normalised Difference Vegetation Index, mainly at a 30 m altitude. However, greater spectral separability was obtained between vegetation and bare soil with the index NDVI. These results suggest that an agreement among spectral and spatial resolutions is needed to optimise the flight mission according to every agronomical objective as affected by the size of the smaller object to be discriminated (weed plants or weed patches.

Configuration and specifications of an Unmanned Aerial Vehicle (UAV) for early site specific weed management.

Science.gov (United States)

Torres-Sánchez, Jorge; López-Granados, Francisca; De Castro, Ana Isabel; Peña-Barragán, José Manuel

2013-01-01

A new aerial platform has risen recently for image acquisition, the Unmanned Aerial Vehicle (UAV). This article describes the technical specifications and configuration of a UAV used to capture remote images for early season site- specific weed management (ESSWM). Image spatial and spectral properties required for weed seedling discrimination were also evaluated. Two different sensors, a still visible camera and a six-band multispectral camera, and three flight altitudes (30, 60 and 100 m) were tested over a naturally infested sunflower field. The main phases of the UAV workflow were the following: 1) mission planning, 2) UAV flight and image acquisition, and 3) image pre-processing. Three different aspects were needed to plan the route: flight area, camera specifications and UAV tasks. The pre-processing phase included the correct alignment of the six bands of the multispectral imagery and the orthorectification and mosaicking of the individual images captured in each flight. The image pixel size, area covered by each image and flight timing were very sensitive to flight altitude. At a lower altitude, the UAV captured images of finer spatial resolution, although the number of images needed to cover the whole field may be a limiting factor due to the energy required for a greater flight length and computational requirements for the further mosaicking process. Spectral differences between weeds, crop and bare soil were significant in the vegetation indices studied (Excess Green Index, Normalised Green-Red Difference Index and Normalised Difference Vegetation Index), mainly at a 30 m altitude. However, greater spectral separability was obtained between vegetation and bare soil with the index NDVI. These results suggest that an agreement among spectral and spatial resolutions is needed to optimise the flight mission according to every agronomical objective as affected by the size of the smaller object to be discriminated (weed plants or weed patches).

Developpement of an original aerial-based inventory method: first steps towards the use of mini Unmanned Areal Vehicle in elephant inventory

OpenAIRE

Lisein, Jonathan; Vermeulen, Cédric; Bouché, Philippe; Lejeune, Philippe

2012-01-01

This research aims at developping a new methodology for counting large mammals by means of an unmanned aerial vehicle. Test flights have been performed in the game ranch of Nazinga (Burkina Faso) during the month of february 2012. Aerial images shows that elephant detection is quite feasible. The systems still requires a lot of improvements in order to be able to cover bigger surfaces for a given pixels resolution. Nevertheless, this method seems very promissing and could advantageously repla...

Lossless Image Compression Based on Multiple-Tables Arithmetic Coding

Directory of Open Access Journals (Sweden)

Rung-Ching Chen

2009-01-01

Full Text Available This paper is intended to present a lossless image compression method based on multiple-tables arithmetic coding (MTAC method to encode a gray-level image f. First, the MTAC method employs a median edge detector (MED to reduce the entropy rate of f. The gray levels of two adjacent pixels in an image are usually similar. A base-switching transformation approach is then used to reduce the spatial redundancy of the image. The gray levels of some pixels in an image are more common than those of others. Finally, the arithmetic encoding method is applied to reduce the coding redundancy of the image. To promote high performance of the arithmetic encoding method, the MTAC method first classifies the data and then encodes each cluster of data using a distinct code table. The experimental results show that, in most cases, the MTAC method provides a higher efficiency in use of storage space than the lossless JPEG2000 does.

Collection and Analysis of Crowd Data with Aerial, Rooftop, and Ground Views

Science.gov (United States)

2014-11-10

collected these datasets using different aircrafts. Erista 8 HL OctaCopter is a heavy-lift aerial platform capable of using high-resolution cinema ...is another high-resolution camera that is cinema grade and high quality, with the capability of capturing videos with 4K resolution at 30 frames per...292.58 Imaging Systems and Accessories Blackmagic Production Camera 4 Crowd Counting using 4K Cameras High resolution cinema grade digital video

OVERVIEW OF MODERN RESEARCH OF LANDSLIDES ACCORDING TO AERIAL AND SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

K. M. Lyapishev

2015-01-01

Full Text Available This article is an overview of researches of landslides using remote sensing methods such as aerial photography, satellite images, radar interferometry, and their combination with the use of GIS technology. Modern methods of investigation of landslides are very diverse. The authors propose different approaches to the identification, classification and monitoring of landslides. Data analysis techniques can help in creating more sophisticated approach to the analysis of landslides.

Light chain deposition disease in multiple myeloma: MR imaging features correlated with histopathological findings

International Nuclear Information System (INIS)

Baur, A.; Staebler, A.; Reiser, M.; Lamerz, R.; Bartl, R.

1998-01-01

The clinical, histopathological, and imaging findings on MRI of a 56-year-old woman with light chain deposition disease occurring in multiple myeloma are presented. Light chain deposition disease is a variant of multiple myeloma with distinct clinical and histological characteristics. MRI of this patient also revealed an infiltration pattern in the bone marrow distinct from that of typical multiple myeloma. Multiple small foci of low signal intensity were present on T1- and T2-weighted spin echo and STIR images, corresponding to conglomerates of light chains in bone marrow biopsy. Contrast-enhanced T1-weighted spin echo images show diffuse enhancement of 51% over all vertebral bodies, with a minor enhancement of the focal conglomerates of light chains. Light chain deposition disease in multiple myeloma should be added to the list of those few entities with normal radiographs and discrete low-signal marrow lesions on T1- and T2-weighted spin echo pulse sequences. (orig.)

Submicron, soft x-ray fluorescence imaging

International Nuclear Information System (INIS)

La Fontaine, B.; MacDowell, A.A.; Tan, Z.; White, D.L.; Taylor, G.N.; Wood, O.R. II; Bjorkholm, J.E.; Tennant, D.M.; Hulbert, S.L.

1995-01-01

Submicron fluorescence imaging of soft x-ray aerial images, using a high resolution fluorescent crystal is reported. Features as small as 0.1 μm were observed using a commercially available single-crystal phosphor, STI-F10G (Star Tech Instruments Inc. P. O. Box 2536, Danbury, CT 06813-2536), excited with 139 A light. Its quantum efficiency was estimated to be 5--10 times that of sodium salicylate and to be constant over a broad spectral range from 30 to 400 A. A comparison with a terbium-activated yttrium orthosilicate fluorescent crystal is also presented. Several applications, such as the characterization of the aerial images produced by deep ultraviolet or extreme ultraviolet lithographic exposure tools, are envisaged

Design and development of a Compact Aerial Radiation Monitoring System (CARMS)

International Nuclear Information System (INIS)

Raman, N.; Chaudhury, Probal; Padmanabhan, N.; Pradeepkumar, K.S.; Sharma, D.N.

2005-01-01

Operation of nuclear facilities, increasing usage of radioisotopes in industrial, scientific and medical applications and transport of nuclear and radioactive materials may have impact on the surrounding environment. There is thus a need to periodically monitor the environmental radiation background all over the country and particularly around the nuclear facilities for assessing any possible impact on the environment. Preparedness required for response to emergencies caused due to radiological/nuclear incidents/ accidents or due to radiological/nuclear terrorism also demands state of the art systems and methodology for quick assessment of radiological impact over large affected areas. In order to meet these requirements, a Compact Aerial Radiation Monitoring System (CARMS) has been designed and developed. This system is battery operated, portable and rugged for mobile radiation monitoring and can be placed in aerial platforms like helicopters or Unmanned Aerial Vehicles (UAVs) for unattended operation. CARMS uses energy compensated multiple GM detectors for enhancing sensitivity and is attached with commercially available Global Positioning System (GPS) for online acquisition of positional coordinates with time. The AT89LV52 microcontroller used in the system tags the dose rate data with time and positional information and stores contiguously in a serial data memory for radiological mapping of the area surveyed using any mobile platform such as aircraft/train/boat/road vehicle. The system consumes ∼150 mA including the GPS at 12 V DC enabling ∼50 hours of continuous monitoring with a 7 Ah battery source. The system has been used in aerial, rail and road based environmental radiation surveys carried out at various places of the country. With PC support, the system can map the radiological status online onto the map of the area being surveyed to help decision-making on countermeasures during the survey. (author)

Seven-Tesla Magnetization Transfer Imaging to Detect Multiple Sclerosis White Matter Lesions.

Science.gov (United States)

Chou, I-Jun; Lim, Su-Yin; Tanasescu, Radu; Al-Radaideh, Ali; Mougin, Olivier E; Tench, Christopher R; Whitehouse, William P; Gowland, Penny A; Constantinescu, Cris S

2018-03-01

Fluid-attenuated inversion recovery (FLAIR) imaging at 3 Tesla (T) field strength is the most sensitive modality for detecting white matter lesions in multiple sclerosis. While 7T FLAIR is effective in detecting cortical lesions, it has not been fully optimized for visualization of white matter lesions and thus has not been used for delineating lesions in quantitative magnetic resonance imaging (MRI) studies of the normal appearing white matter in multiple sclerosis. Therefore, we aimed to evaluate the sensitivity of 7T magnetization-transfer-weighted (MT w ) images in the detection of white matter lesions compared with 3T-FLAIR. Fifteen patients with clinically isolated syndrome, 6 with multiple sclerosis, and 10 healthy participants were scanned with 7T 3-dimensional (D) MT w and 3T-2D-FLAIR sequences on the same day. White matter lesions visible on either sequence were delineated. Of 662 lesions identified on 3T-2D-FLAIR images, 652 were detected on 7T-3D-MT w images (sensitivity, 98%; 95% confidence interval, 97% to 99%). The Spearman correlation coefficient between lesion loads estimated by the two sequences was .910. The intrarater and interrater reliability for 7T-3D-MT w images was good with an intraclass correlation coefficient (ICC) of 98.4% and 81.8%, which is similar to that for 3T-2D-FLAIR images (ICC 96.1% and 96.7%). Seven-Tesla MT w sequences detected most of the white matter lesions identified by FLAIR at 3T. This suggests that 7T-MT w imaging is a robust alternative for detecting demyelinating lesions in addition to 3T-FLAIR. Future studies need to compare the roles of optimized 7T-FLAIR and of 7T-MT w imaging. © 2017 The Authors. Journal of Neuroimaging published by Wiley Periodicals, Inc. on behalf of American Society of Neuroimaging.

COLOR IMAGE RETRIEVAL BASED ON FEATURE FUSION THROUGH MULTIPLE LINEAR REGRESSION ANALYSIS

Directory of Open Access Journals (Sweden)

K. Seetharaman

2015-08-01

Full Text Available This paper proposes a novel technique based on feature fusion using multiple linear regression analysis, and the least-square estimation method is employed to estimate the parameters. The given input query image is segmented into various regions according to the structure of the image. The color and texture features are extracted on each region of the query image, and the features are fused together using the multiple linear regression model. The estimated parameters of the model, which is modeled based on the features, are formed as a vector called a feature vector. The Canberra distance measure is adopted to compare the feature vectors of the query and target images. The F-measure is applied to evaluate the performance of the proposed technique. The obtained results expose that the proposed technique is comparable to the other existing techniques.

The sky is the limit? 20 years of small-format aerial photography taken from UAS for monitoring geomorphological processes

Science.gov (United States)

Marzolff, Irene

2014-05-01

One hundred years after the first publication on aerial photography taken from unmanned aerial platforms (Arthur Batut 1890), small-format aerial photography (SFAP) became a distinct niche within remote sensing during the 1990s. Geographers, plant biologists, archaeologists and other researchers with geospatial interests re-discovered the usefulness of unmanned platforms for taking high-resolution, low-altitude photographs that could then be digitized and analysed with geographical information systems, (softcopy) photogrammetry and image processing techniques originally developed for digital satellite imagery. Even before the ubiquity of digital consumer-grade cameras and 3D analysis software accessible to the photogrammetric layperson, do-it-yourself remote sensing using kites, blimps, drones and micro air vehicles literally enabled the questing researcher to get their own pictures of the world. As a flexible, cost-effective method, SFAP offered images with high spatial and temporal resolutions that could be ideally adapted to the scales of landscapes, forms and distribution patterns to be monitored. During the last five years, this development has been significantly accelerated by the rapid technological advancements of GPS navigation, autopiloting and revolutionary softcopy-photogrammetry techniques. State-of-the-art unmanned aerial systems (UAS) now allow automatic flight planning, autopilot-controlled aerial surveys, ground control-free direct georeferencing and DEM plus orthophoto generation with centimeter accuracy, all within the space of one day. The ease of use of current UAS and processing software for the generation of high-resolution topographic datasets and spectacular visualizations is tempting and has spurred the number of publications on these issues - but which advancements in our knowledge and understanding of geomorphological processes have we seen and can we expect in the future? This presentation traces the development of the last two decades

Near real-time shadow detection and removal in aerial motion imagery application

Science.gov (United States)

Silva, Guilherme F.; Carneiro, Grace B.; Doth, Ricardo; Amaral, Leonardo A.; Azevedo, Dario F. G. de

2018-06-01

This work presents a method to automatically detect and remove shadows in urban aerial images and its application in an aerospace remote monitoring system requiring near real-time processing. Our detection method generates shadow masks and is accelerated by GPU programming. To obtain the shadow masks, we converted images from RGB to CIELCh model, calculated a modified Specthem ratio, and applied multilevel thresholding. Morphological operations were used to reduce shadow mask noise. The shadow masks are used in the process of removing shadows from the original images using the illumination ratio of the shadow/non-shadow regions. We obtained shadow detection accuracy of around 93% and shadow removal results comparable to the state-of-the-art while maintaining execution time under real-time constraints.

Employing unmanned aerial vehicle to monitor the health condition of wind turbines

Science.gov (United States)

Huang, Yishuo; Chiang, Chih-Hung; Hsu, Keng-Tsang; Cheng, Chia-Chi

2018-04-01

Unmanned aerial vehicle (UAV) can gather the spatial information of huge structures, such as wind turbines, that can be difficult to obtain with traditional approaches. In this paper, the UAV used in the experiments is equipped with high resolution camera and thermal infrared camera. The high resolution camera can provide a series of images with resolution up to 10 Megapixels. Those images can be used to form the 3D model using the digital photogrammetry technique. By comparing the 3D scenes of the same wind turbine at different times, possible displacement of the supporting tower of the wind turbine, caused by ground movement or foundation deterioration may be determined. The recorded thermal images are analyzed by applying the image segmentation methods to the surface temperature distribution. A series of sub-regions are separated by the differences of the surface temperature. The high-resolution optical image and the segmented thermal image are fused such that the surface anomalies are more easily identified for wind turbines.

Locating chimpanzee nests and identifying fruiting trees with an unmanned aerial vehicle.

Science.gov (United States)

van Andel, Alexander C; Wich, Serge A; Boesch, Christophe; Koh, Lian Pin; Robbins, Martha M; Kelly, Joseph; Kuehl, Hjalmar S

2015-10-01

Monitoring of animal populations is essential for conservation management. Various techniques are available to assess spatiotemporal patterns of species distribution and abundance. Nest surveys are often used for monitoring great apes. Quickly developing technologies, including unmanned aerial vehicles (UAVs) can be used to complement these ground-based surveys, especially for covering large areas rapidly. Aerial surveys have been used successfully to detect the nests of orang-utans. It is unknown if such an approach is practical for African apes, which usually build their nests at lower heights, where they might be obscured by forest canopy. In this 2-month study, UAV-derived aerial imagery was used for two distinct purposes: testing the detectability of chimpanzee nests and identifying fruiting trees used by chimpanzees in Loango National Park (Gabon). Chimpanzee nest data were collected through two approaches: we located nests on the ground and then tried to detect them in UAV photos and vice versa. Ground surveys were conducted using line transects, reconnaissance trails, and opportunistic sampling during which we detected 116 individual nests in 28 nest groups. In complementary UAV images we detected 48% of the individual nests (68% of nest groups) in open coastal forests and 8% of individual nests (33% of nest groups) in closed canopy inland forests. The key factor for nest detectability in UAV imagery was canopy openness. Data on fruiting trees were collected from five line transects. In 122 UAV images 14 species of trees (N = 433) were identified, alongside 37 tree species (N = 205) in complementary ground surveys. Relative abundance of common tree species correlated between ground and UAV surveys. We conclude that UAVs have great potential as a rapid assessment tool for detecting chimpanzee presence in forest with open canopy and assessing fruit tree availability. UAVs may have limited applicability for nest detection in closed canopy forest. �

Exploring manifold structure of face images via multiple graphs

KAUST Repository

Alghamdi, Masheal

2013-01-01

Geometric structure in the data provides important information for face image recognition and classification tasks. Graph regularized non-negative matrix factorization (GrNMF) performs well in this task. However, it is sensitive to the parameters selection. Wang et al. proposed multiple graph regularized non-negative matrix factorization (MultiGrNMF) to solve the parameter selection problem by testing it on medical images. In this paper, we introduce the MultiGrNMF algorithm in the context of still face Image classification, and conduct a comparative study of NMF, GrNMF, and MultiGrNMF using two well-known face databases. Experimental results show that MultiGrNMF outperforms NMF and GrNMF for most cases.

Exploring manifold structure of face images via multiple graphs

KAUST Repository

Alghamdi, Masheal

2013-12-24

Geometric structure in the data provides important information for face image recognition and classification tasks. Graph regularized non-negative matrix factorization (GrNMF) performs well in this task. However, it is sensitive to the parameters selection. Wang et al. proposed multiple graph regularized non-negative matrix factorization (MultiGrNMF) to solve the parameter selection problem by testing it on medical images. In this paper, we introduce the MultiGrNMF algorithm in the context of still face Image classification, and conduct a comparative study of NMF, GrNMF, and MultiGrNMF using two well-known face databases. Experimental results show that MultiGrNMF outperforms NMF and GrNMF for most cases.

Status of thermal imaging technology as applied to conservation-update 1

Energy Technology Data Exchange (ETDEWEB)

Snow, F.J.; Wood, J.T.; Barthle, R.C.

1980-07-01

This document updates the 1978 report on the status of thermal imaging technology as applied to energy conservation in buildings. Thermal imaging technology is discussed in terms of airborne surveys, ground survey programs, and application needs such as standards development and lower cost equipment. Information on the various thermal imaging devices was obtained from manufacturer's standard product literature. Listings are provided of infrared projects of the DOE building diagnostics program, of aerial thermographic firms, and of aerial survey programs. (LCL)

Single image super-resolution using locally adaptive multiple linear regression.

Science.gov (United States)

Yu, Soohwan; Kang, Wonseok; Ko, Seungyong; Paik, Joonki

2015-12-01

This paper presents a regularized superresolution (SR) reconstruction method using locally adaptive multiple linear regression to overcome the limitation of spatial resolution of digital images. In order to make the SR problem better-posed, the proposed method incorporates the locally adaptive multiple linear regression into the regularization process as a local prior. The local regularization prior assumes that the target high-resolution (HR) pixel is generated by a linear combination of similar pixels in differently scaled patches and optimum weight parameters. In addition, we adapt a modified version of the nonlocal means filter as a smoothness prior to utilize the patch redundancy. Experimental results show that the proposed algorithm better restores HR images than existing state-of-the-art methods in the sense of the most objective measures in the literature.

7 CFR 1755.507 - Aerial cable services.

Science.gov (United States)

2010-01-01

... requirements: (1) Strand supported lashed construction shall be used. (2) Where practicable a 5/16 in. (8 mm...) Construction on poles shall comply with applicable construction drawings for regular line construction. Aerial... copper and fiber optic cables. (4) Where practicable, aerial cable shall pass under electrical guys...

Aerial thermography from low-cost UAV for the generation of thermographic digital terrain models

Science.gov (United States)

Lagüela, S.; Díaz-Vilariño, L.; Roca, D.; Lorenzo, H.

2015-03-01

Aerial thermography is performed from a low-cost aerial vehicle, copter type, for the acquisition of data of medium-size areas, such as neighbourhoods, districts or small villages. Thermographic images are registered in a mosaic subsequently used for the generation of a thermographic digital terrain model (DTM). The thermographic DTM can be used with several purposes, from classification of land uses according to their thermal response to the evaluation of the building prints as a function of their energy performance, land and water management. In the particular case of buildings, apart from their individual evaluation and roof inspection, the availability of thermographic information on a DTM allows for the spatial contextualization of the buildings themselves and the general study of the surrounding area for the detection of global effects such as heat islands.

Utilization of multiple frequencies in 3D nonlinear microwave imaging

DEFF Research Database (Denmark)

Jensen, Peter Damsgaard; Rubæk, Tonny; Mohr, Johan Jacob

2012-01-01

The use of multiple frequencies in a nonlinear microwave algorithm is considered. Using multiple frequencies allows for obtaining the improved resolution available at the higher frequencies while retaining the regularizing effects of the lower frequencies. However, a number of different challenges...... at lower frequencies are used as starting guesses for reconstructions at higher frequencies. The performance is illustrated using simulated 2-D data and data obtained with the 3-D DTU microwave imaging system....

Images crossing borders: image and workflow sharing on multiple levels.

Science.gov (United States)

Ross, Peeter; Pohjonen, Hanna

2011-04-01

Digitalisation of medical data makes it possible to share images and workflows between related parties. In addition to linear data flow where healthcare professionals or patients are the information carriers, a new type of matrix of many-to-many connections is emerging. Implementation of shared workflow brings challenges of interoperability and legal clarity. Sharing images or workflows can be implemented on different levels with different challenges: inside the organisation, between organisations, across country borders, or between healthcare institutions and citizens. Interoperability issues vary according to the level of sharing and are either technical or semantic, including language. Legal uncertainty increases when crossing national borders. Teleradiology is regulated by multiple European Union (EU) directives and legal documents, which makes interpretation of the legal system complex. To achieve wider use of eHealth and teleradiology several strategic documents were published recently by the EU. Despite EU activities, responsibility for organising, providing and funding healthcare systems remains with the Member States. Therefore, the implementation of new solutions requires strong co-operation between radiologists, societies of radiology, healthcare administrators, politicians and relevant EU authorities. The aim of this article is to describe different dimensions of image and workflow sharing and to analyse legal acts concerning teleradiology in the EU.

Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees

DEFF Research Database (Denmark)

Garcia Ruiz, Francisco Jose; Sankaran, Sindhuja; Maja, Joe Mari

2013-01-01

and HLB-infected trees. During classification studies, accuracies in the range of 67–85% and false negatives from 7% to 32% were acquired from UAV-based data; while corresponding values were 61–74% and 28–45% with aircraft-based data. Among the tested classification algorithms, support vector machine (SVM......) with kernel resulted in better performance than other methods such as SVM (linear), linear discriminant analysis and quadratic discriminant analysis. Thus, high-resolution aerial sensing has good prospect for the detection of HLB-infected trees....

Measuring multiple nano-textured areas simultaneously with imaging scatterometry

DEFF Research Database (Denmark)

Madsen, Jonas Skovlund; Hansen, Poul Erik; Bilenberg, Brian

2017-01-01

and areas with defects can be avoided. These advantages make imaging scatterometry a very effective and user-friendly characterization method and allow us to determine the homogeneity of a nano- Textured surface by performing pixel-wise analyses. In the analysis an inverse modelling approach is used, where...... measured diffraction efficiencies are compared to simulated diffraction efficiencies using a least-square fitting approach. We demonstrate an imaging scatterometry setup built into an optical microscope. The setup is capable of measuring multiple 2D gratings with pitches of 200 nm simultaneously...

Reconstructing building mass models from UAV images

KAUST Repository

Li, Minglei

2015-07-26

We present an automatic reconstruction pipeline for large scale urban scenes from aerial images captured by a camera mounted on an unmanned aerial vehicle. Using state-of-the-art Structure from Motion and Multi-View Stereo algorithms, we first generate a dense point cloud from the aerial images. Based on the statistical analysis of the footprint grid of the buildings, the point cloud is classified into different categories (i.e., buildings, ground, trees, and others). Roof structures are extracted for each individual building using Markov random field optimization. Then, a contour refinement algorithm based on pivot point detection is utilized to refine the contour of patches. Finally, polygonal mesh models are extracted from the refined contours. Experiments on various scenes as well as comparisons with state-of-the-art reconstruction methods demonstrate the effectiveness and robustness of the proposed method.

1935 15' Quad #292 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #246 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #364 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #056 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #032 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #063 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #147 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #265 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #222 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #122 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #004 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #410 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #368 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #180 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #349 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #266 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #130 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #387 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #223 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #127 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #350 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #009 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #062 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #221 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #243 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #155 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #059 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #152 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #226 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #126 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #037 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #227 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #132 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #298 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #361 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #100 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #490 Aerial Photo Mosaic Index

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

1935 15' Quad #181 Aerial Photo Mosaic Index